Contribution to proactivity in mobile context-aware recommender systems

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(1)UNIVERSIDAD POLITÉCNICA DE MADRID Escuela Técnica Superior de Ingenieros de Telecomunicación. CONTRIBUTION TO PROACTIVITY IN MOBILE CONTEXT-AWARE RECOMMENDER SYSTEMS. TESIS DOCTORAL. Daniel Gallego Vico Ingeniero de Telecomunicación Madrid, España Septiembre, 2013.

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(3) Departamento de Ingeniería de Sistemas Telemáticos Escuela Técnica Superior de Ingenieros de Telecomunicación. CONTRIBUTION TO PROACTIVITY IN MOBILE CONTEXT-AWARE RECOMMENDER SYSTEMS. TESIS DOCTORAL. Autor: Daniel Gallego Vico Ingeniero de Telecomunicación Director: Gabriel Huecas Fernández-Toribio Doctor Ingeniero de Telecomunicación. Septiembre, 2013.

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(5) Tribunal nombrado por el Magnífico. y Excelentísimo. Sr. Rector de la Universidad Politécnica de Madrid, el día 16 de Septiembre de 2013.. Presidente:. Vocal:. Vocal:. Vocal:. Secretario:. Suplente:. Suplente:. Realizado el acto de defensa y lectura de la Tesis el día 26 de Septiembre de 2013 en Madrid, habiendo obtenido la calificación de. El presidente,. El secretario,. Los vocales,.

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(7) ABSTRACT. Recommender systems are powerful information filtering tools which offer users personalized suggestions about items whose aim is to satisfy their needs. Traditionally the information used to make recommendations has been based on users’ ratings or data on the item’s consumption history and transactions carried out in the system. However, due to the remarkable growth in mobile devices in our society, new opportunities have arisen to improve these systems by implementing them in ubiquitous environments which provide rich context-awareness information on their location or current activity. Because of this current all-mobile lifestyle, users are socially connected permanently, which allows their context to be enhanced not only with physical information, but also with a social dimension. As a result of these novel contextual data sources, the advent of mobile Context-Aware Recommender Systems (CARS) as a research area has appeared to improve the level of personalization in recommendation. On the other hand, this new scenario in which users have their mobile devices with them all the time offers the possibility of looking into new ways of making recommendations. Evolving the traditional user request-response pattern to a proactive approach is now possible as a result of this rich contextual scenario. Thus, the key idea is that recommendations are made to the user when the current situation is appropriate, attending to the available contextual information without an explicit user request being necessary. This dissertation proposes a set of models, algorithms and methods to incorporate proactivity into mobile CARS, while the impact of proactivity is studied in terms of user experience to extract significant outcomes as to "what", "when" and "how" proactive recommendations have to be notified to users. To this end, the development of this dissertation starts from the proposal of a general architecture for building mobile CARS in scenarios with rich social data along with a new way of managing a recommendation process through a REST interface to make this architecture multi-device and cross-platform compatible. Details as regards its implementation and evaluation in a Spanish banking scenario are provided to validate its usefulness and user acceptance. After that, a novel model is presented for proactivity in mobile CARS which shows the key ideas related to decide when a situation warrants a proactive recommendation by establishing algorithms that represent the relationship between the appropriateness of a situation and the suitability of the candidate items to be recommended. A validation of these ideas in the area of e-learning authoring tools is also presented. Following the previous model, this dissertation presents the design and implementation of new mobile user interfaces for proactive notifications. The results of an evaluation among users testing these novel interfaces is also shown to study the impact of proactivity in the user experience of mobile CARS, while significant factors associated to proactivity are also identified. The last stage of this dissertation merges the previous outcomes to design a new methodology to calculate the appropriateness of a situation so as to incorporate proactivity into mobile CARS. Additionally, this work provides details about its validation in a European e-learning social network in which the whole architecture and proactive recommendation model together with its methods have been implemented. Finally, this dissertation opens up a discussion about the conclusions obtained throughout this research, resulting in useful information from the different design and implementation stages of proactive mobile CARS..

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(9) RESUMEN Los sistemas de recomendación son potentes herramientas de filtrado de información que permiten a usuarios solicitar sugerencias sobre ítems que cubran sus necesidades. Tradicionalmente estas recomendaciones han estado basadas en opiniones de los mismos, así como en datos obtenidos de su consumo histórico o comportamiento en el propio sistema. Sin embargo, debido a la gran penetración y uso de los dispositivos móviles en nuestra sociedad, han surgido nuevas oportunidades en el campo de los sistemas de recomendación móviles gracias a la información contextual que se puede obtener sobre la localización o actividad de los usuarios. Debido a este estilo de vida en el que todo tiende a la movilidad y donde los usuarios están plenamente interconectados, la información contextual no sólo es física, sino que también adquiere una dimensión social. Todo esto ha dado lugar a una nueva área de investigación relacionada con los Sistemas de Recomendación Basados en Contexto (CARS) móviles donde se busca incrementar el nivel de personalización de las recomendaciones al usar dicha información. Por otro lado, este nuevo escenario en el que los usuarios llevan en todo momento un terminal móvil consigo abre la puerta a nuevas formas de recomendar. Sustituir el tradicional patrón de uso basado en petición-respuesta para evolucionar hacia un sistema proactivo es ahora posible. Estos sistemas deben identificar el momento más adecuado para generar una recomendación sin una petición explícita del usuario, siendo para ello necesario analizar su contexto. Esta tesis doctoral propone un conjunto de modelos, algoritmos y métodos orientados a incorporar proactividad en CARS móviles, a la vez que se estudia el impacto que este tipo de recomendaciones tienen en la experiencia de usuario con el fin de extraer importantes conclusiones sobre "qué", "cuándo" y "cómo" se debe notificar proactivamente. Con este propósito, se comienza planteando una arquitectura general para construir CARS móviles en escenarios sociales. Adicionalmente, se propone una nueva forma de representar el proceso de recomendación a través de una interfaz REST, lo que permite crear una arquitectura independiente de dispositivo y plataforma. Los detalles de su implementación tras su puesta en marcha en el entorno bancario español permiten asimismo validar el sistema construido. Tras esto se presenta un novedoso modelo para incorporar proactividad en CARS móviles. Éste muestra las ideas principales que permiten analizar una situación para decidir cuándo es apropiada una recomendación proactiva. Para ello se presentan algoritmos que establecen relaciones entre lo propicia que es una situación y cómo esto influye en los elementos a recomendar. Asimismo, para demostrar la viabilidad de este modelo se describe su aplicación a un escenario de recomendación para herramientas de creación de contenidos educativos. Siguiendo el modelo anterior, se presenta el diseño e implementación de nuevos interfaces móviles de usuario para recomendaciones proactivas, así como los resultados de su evaluación entre usuarios, lo que aportó importantes conclusiones para identificar cuáles son los factores más relevantes a considerar en el diseño de sistemas proactivos. A raíz de los resultados anteriores, el último punto de esta tesis presenta una metodología para calcular cuán apropiada es una situación de cara a recomendar de manera proactiva siguiendo el modelo propuesto. Como conclusión, se describe la validación llevada a cabo tras la aplicación de la arquitectura, modelo de recomendación y métodos descritos en este trabajo en una red social de aprendizaje europea. Finalmente, esta tesis discute las conclusiones obtenidas a lo largo de la extensa investigación llevada a cabo, y que ha propiciado la consecución de una buena base teórica y práctica para la creación de sistemas de recomendación móviles proactivos basados en información contextual..

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(11) Acknowledgments.

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(13) Contents. Abstract Resumen Acknowledgments List of Illustrations List of Tables List of Acronyms. 1 Introduction 1.1 1.2 1.3. 1. Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Structure of this Document . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 2 State of the Art 2.1. 2.2. 2.3. 2.4. 2.5. Recommender Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 General Overview . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Data and Knowledge Sources . . . . . . . . . . . . . . . . . . 2.1.3 Recommendation Techniques . . . . . . . . . . . . . . . . . . 2.1.4 Data Mining Methods . . . . . . . . . . . . . . . . . . . . . . 2.1.5 Recommender Systems and Human Computer Interaction . . . Context-Aware Recommender Systems . . . . . . . . . . . . . . . . . 2.2.1 Defining Context . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Modeling Contextual Information in Recommender Systems . . 2.2.3 Obtaining Contextual Information . . . . . . . . . . . . . . . . 2.2.4 Paradigms for Incorporating Context in Recommender Systems Mobile Recommender Systems . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Mobile Context-Aware Recommendation Systems . . . . . . . 2.3.2 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proactivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Minimizing the Costs of Proactivity . . . . . . . . . . . . . . . 2.4.2 Processing Interruptions . . . . . . . . . . . . . . . . . . . . . 2.4.3 Deciding about Notifications . . . . . . . . . . . . . . . . . . . 2.4.4 Notification Cues . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.5 Timing a Notification . . . . . . . . . . . . . . . . . . . . . . . 2.4.6 Adapting Notification Modalities to User Preferences . . . . . . 2.4.7 Proactivity in Recommender Systems . . . . . . . . . . . . . . Related Recommender Systems Research: Application Fields . . . . . . 2.5.1 Banking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 3 4 7. 11 . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. 11 11 15 19 25 29 33 34 37 40 40 44 45 46 49 49 51 52 53 54 55 55 57 57 58.

(14) CONTENTS. 3 An Architecture for Social Mobile CARS 3.1 3.2 3.3. 3.4. 3.5. 3.6 3.7. Introduction . . . . . . . . . . . . . . . . . Objectives . . . . . . . . . . . . . . . . . . Design . . . . . . . . . . . . . . . . . . . . 3.3.1 Recommendation Model . . . . . . 3.3.2 Architecture . . . . . . . . . . . . . Implementation . . . . . . . . . . . . . . . 3.4.1 Scenario . . . . . . . . . . . . . . . 3.4.2 Banking Data Anonymization . . . 3.4.3 Recommender . . . . . . . . . . . 3.4.4 Application Manager and API . . . 3.4.5 Mobile Client . . . . . . . . . . . . Evaluation and Results . . . . . . . . . . . 3.5.1 Description and Objectives . . . . . 3.5.2 Demographics and Data Collection 3.5.3 Results . . . . . . . . . . . . . . . 3.5.4 Discussion . . . . . . . . . . . . . Contribution . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . .. 63 . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design and Implementation of the Mobile Application . . . . . 5.3.1 Design Guidelines for Proactive Notifications . . . . . . 5.3.2 Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Technological Decisions . . . . . . . . . . . . . . . . . 5.3.4 Mobile User Interfaces for Proactive Recommendations 5.3.5 User Interfaces for Visualizing Recommended Items . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. 4 A Model for Proactivity in Mobile CARS 4.1 4.2 4.3. 4.4. 4.5 4.6. Introduction . . . . . . . . . . . . . . . . . Objectives . . . . . . . . . . . . . . . . . . Design . . . . . . . . . . . . . . . . . . . . 4.3.1 Challenges and Requirements . . . 4.3.2 Context . . . . . . . . . . . . . . . 4.3.3 Process Overview . . . . . . . . . . 4.3.4 Phase I: Situation Assessment . . . 4.3.5 Phase II: Item Assessment . . . . . 4.3.6 User Feedback . . . . . . . . . . . Application to E-Learning Authoring Tools 4.4.1 Motivation . . . . . . . . . . . . . 4.4.2 Scenario: Use Cases . . . . . . . . 4.4.3 Model Adaptation . . . . . . . . . Contribution . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . .. 95. 5 Proactivity Impact in Mobile CARS User Experience 5.1 5.2 5.3. 63 64 66 66 72 75 75 78 79 83 86 88 88 88 88 90 91 92. 95 96 97 97 98 99 99 100 102 102 102 103 106 111 112. 113 113 114 115 115 116 116 118 121.

(15) CONTENTS 5.4. 5.5. 5.6 5.7. Evaluation and Results . . . . . . . . . . . 5.4.1 Description and Objectives . . . . . 5.4.2 Demographics and Data Collection 5.4.3 Results . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . 5.5.1 Limitations of the Study . . . . . . 5.5.2 Proactivity Impact . . . . . . . . . 5.5.3 Recommendations Visualization . . Contribution . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. 6 Methods to Incorporate Proactivity into Mobile CARS 6.1 6.2 6.3 6.4. Introduction . . . . . . . . . . . . . . . . . . . . Objectives . . . . . . . . . . . . . . . . . . . . . Recommendation Model . . . . . . . . . . . . . Scenario: The Virtual Science Hub . . . . . . . . 6.4.1 Motivation: Requirements and Challenges 6.5 Phase I: Social Context Generation . . . . . . . . 6.5.1 Method . . . . . . . . . . . . . . . . . . 6.5.2 Evaluation and results . . . . . . . . . . 6.6 Phase II: Situation Assessment . . . . . . . . . . 6.6.1 Method . . . . . . . . . . . . . . . . . . 6.6.2 Evaluation and Results . . . . . . . . . . 6.6.3 Application to ViSH: Features and Values 6.7 Phase III: Item Assessment . . . . . . . . . . . . 6.7.1 Method . . . . . . . . . . . . . . . . . . 6.7.2 Evaluation and Results . . . . . . . . . . 6.7.3 Application to ViSH: Features and Values 6.8 Discussion . . . . . . . . . . . . . . . . . . . . . 6.8.1 Limitations of the Study . . . . . . . . . 6.8.2 Phase I . . . . . . . . . . . . . . . . . . 6.8.3 Phase II . . . . . . . . . . . . . . . . . . 6.8.4 Phase III . . . . . . . . . . . . . . . . . 6.9 Displaying Proactive Recommendations in ViSH 6.10 Contribution . . . . . . . . . . . . . . . . . . . . 6.11 Conclusions . . . . . . . . . . . . . . . . . . . .. 139. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. 7 Validation and Results 7.1 7.2 7.3. Validation in National Projects . . . 7.1.1 Perdidos en la Gran Ciudad Validation in European Projects . . . 7.2.1 GLOBAL excursion . . . . Dissemination of Results . . . . . . 7.3.1 Publications . . . . . . . . . 7.3.2 Research Visit . . . . . . . 7.3.3 Teaching . . . . . . . . . .. 124 124 125 126 131 131 132 135 136 137. 139 140 141 143 144 145 145 149 151 151 154 154 157 157 160 161 163 163 164 165 166 167 170 170. 173 . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. 173 173 175 175 176 176 178 179.

(16) CONTENTS. 8 Conclusions 8.1 8.2 8.3 8.4 8.5. What kind of architecture is suitable for building mobile context-aware recommender systems in scenarios with rich social data? . . . . . . . . How can proactivity be incorporated into mobile CARS? . . . . . . . . Which UX factors are considered in proactive recommenders? . . . . . Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. Bibliography. 181 . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. 182 183 185 185 191. 195.

(17) Illustrations. 2.1 2.2 2.3 2.4 2.5 2.6 2.7 3.1 3.2 3.3 3.4 3.5 3.6 3.7. General relationships between items, users and transactions. . . . . . . . . Examples of common used rating scales: unary (like), binary (thumbs up / thumbs down), scalar (five-star and slider). . . . . . . . . . . . . . . . . . . Contextual information hierarchical structure for Location context. . . . . . Multidimensional model for the User × Item × Time recommendation space [Adomavicius 2005a]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General components of traditional recommender systems. . . . . . . . . . . Paradigms for incorporating context in recommender systems. . . . . . . . Screen sizes and densities in Android devices [Android-Developer 2013a]. .. . . .. 16. . . . . . .. 18 38. . . . .. 39 41 41 47. . . . .. . . . .. Model for generating context-aware recommendations using social data in mobile recommender systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Steps to generate the social context. . . . . . . . . . . . . . . . . . . . . . . . . General architecture for building mobile context-aware recommender systems based on social data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Architecture deployed for the banking scenario and its recommendation process flow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smartphone and tablet mobile applications showing a restaurant recommendation. Social clusters distribution by average expense in credit card per year, age and size. Application survey results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 67 68 72 77 87 89 89. 4.1 4.2 4.3. Model for proactivity in mobile context-aware recommender systems. . . . . . . 98 Scenario for recommending in mobile e-Learning authoring tools. . . . . . . . . 105 Model for generating proactive context-aware recommendations in e-Learning authoring tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107. 5.1. Proactive user interface based on status bar notification. Notification icon and message in the status bar (a), and expanded notification (b). . . . . . . . . . . Proactive user interface based on widgets. . . . . . . . . . . . . . . . . . . . Ranking user interface: initial view (a) and after giving feedback (b). . . . . . Map user interface: map visualization (a) and place details (b). . . . . . . . . Evaluation results of status bar notification. . . . . . . . . . . . . . . . . . . Evaluation results of widget notification. . . . . . . . . . . . . . . . . . . . . Comparison between widget (W) and status bar (SB) notifications. . . . . . . Evaluation results of ranking recommendation visualization. . . . . . . . . . Evaluation results of map recommendation visualization. . . . . . . . . . . .. 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9. . . . . . . . . .. . . . . . . . . .. 119 120 122 123 127 129 130 131 132.

(18) ILLUSTRATIONS 6.1 6.2 6.3 6.4 6.5 6.6. Model for generating proactive context-aware recommendations in mobile recommender systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of users and learning objects in the social clusters generated. Context model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation results of appropriateness corresponding to the geographical, temporal, device and activity context feature values. . . . . . . . . . . . . Evaluation results of weights corresponding to every context feature. . . . Visualization of proactive recommendations in ViSH. . . . . . . . . . . .. . . . . 142 . . . . 150 . . . . 151 . . . . 157 . . . . 159 . . . . 168.

(19) Tables. 2.1. Summary of main recommendation techniques. . . . . . . . . . . . . . . . . . .. 19. 3.1 3.2. Summary of HTTP methods available for the REST API resources. . . . . . . . . Context information used in the recommendations process. . . . . . . . . . . . .. 74 82. 5.1 5.2 5.3 5.4. Survey scenarios for proactive resturant recommendations. . . . . . . . . . Scenarios responses for the status bar notification. . . . . . . . . . . . . . . Scenarios responses for the widget notification. . . . . . . . . . . . . . . . Statistics related to the comparison between widget (W) and status bar (SB) notifications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . 125 . . . 126 . . . 128. Context features and their values for the ViSH scenario. . Appropriateness factors of context model features values. Weighting of context model features. . . . . . . . . . . . Applicability factors of context model features values. . .. . . . .. 6.1 6.2 6.3 6.4. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . 129 . . . .. . . . .. 155 158 158 163.

(20) TABLES.

(21) Acronyms. A-GPS Assisted Global Positioning System API Application Program Interface CARS Context-Aware Recommender Systems CF Collaborative Filtering E-Commerce Electronic Commerce E-Learning Electronic Learning EC European Commission GPS Global Positioning System HCI Human Computer Interaction ICT Information and Communication Technology IT Information Technology LO Learning Object M-Commerce Mobile Commerce PC Personal Computer REST Representational State Transfer TEL Technology Enhanced Learning U-Commerce Ubiquitous Commerce UI User Interface UPM Universidad Politecnica de Madrid URI Uniform Resource Identifier URL Uniform Resource Locator UX User eXperience ViSH Virtual Science Hub.

(22) TABLES.

(23) Chapter 1 Introduction Recommender Systems are powerful information filtering tools that are currently becoming widely used in Information Technologies. Since the first recommendation algorithms were proposed in the mid-1990s, the field has evolved very quickly especially in recent years resulting from the Internet information overload phenomenon. Thousands of new articles, blog posts, movies, books and songs among others "items" are available every day. In this scenario, selecting the most suitable one for every person is even more difficult as maybe the best choice is not the most popular, it being really complex for anybody to find it out from among an overwhelming set of options. As a result, recommender systems have proved to be key tools in solving this problem. Companies such as Amazon or Netflix are good examples of the benefits of using recommender systems in their platforms, as important percentages of their sales are achieved from recommendations provided to their users, e.g. 75% of the content watched on Netflix comes from its recommendation engine [Amatriain 2012]. Furthermore, not only are commercial applications implementing these techniques, but also other domains such as e-learning or social networks are using recommendations intensively on a daily basis. As regards social networks, they can add an important dimension to recommender systems design as the level of personalization in the recommendation process may be improved if the system knows more about its users. In fact, a social network is not necessary per se to incorporate social information in the recommendation process.. Several entities such as banks or. telecommunication providers also have detailed personal data of their clients’ behavior, consumption trends, etc. Therefore, by using the social knowledge they have about them, they might generate enhanced recommendations about their products, or even more interestingly, about other items that can be inferred by taking into account aggregated information extracted from their customers’ activity. However, how to merge social information from different sources is still a problem in recommender systems design as they are usually domain-dependent. At the same time, the rapid evolution of mobile devices and data networks in the last decade have contributed to the current all-mobile lifestyle based on the huge market penetration of.

(24) CHAPTER 1. INTRODUCTION. 2. smartphones and tablets. Everything is currently going mobile, and thus recommender systems have evolved to become ubiquitous too. Almost every mobile device currently has a set of sensors such as GPS, accelerometer or compass. They provide the possibility of analyzing the real world related to a user in a more accurate way. By integrating this information in traditional recommender systems, contextual information extracted from these sensors can be used to improve the level of personalization in attending, for example, to the current user’s location or activity. These systems, usually known as Context-Aware Recommender Systems (CARS) [Adomavicius 2011], have given rise to new recommendation algorithms, creating a new paradigm for designing recommender systems in which the system is aware of the current physical, social or temporal environment of the user.. As a consequence of this real-time. knowledge on the user, novel ways of recommending can be looked into to rethink the traditional user request-response pattern commonly used in recommender systems and incorporate proactivity: recommendations are made to the user when the current situation is appropriate without the need for an explicit request. Based on these ideas, this dissertation aims to answer the question: How could proactivity be incorporated into mobile context-aware recommender systems to improve the quality of the recommendations and the user experience in scenarios with rich social data? Keeping in mind this idea, this work addresses three open challenges to the research community: 1. What kind of architecture is suitable for building mobile context-aware recommender systems in scenarios with rich social data? 2. How can proactivity be incorporated into mobile context-aware recommender systems? 3. Which factors need to be considered in the implementation of a proactive recommender system in terms of user experience? This research embraces the study of the improvements achieved in the recommendation paradigm as a result of the synergy between proactivity and mobile CARS in order to take advantage of a changing ubiquitous environment. The main outcome is the proposal of a novel model for generating proactive context-aware recommendations supported by a general architecture to be used in social scenarios, as well as the methods to make recommendations proactively in mobile applications based on user experience..

(25) 1.1. OBJECTIVES. 3. 1.1 Objectives As a result, this dissertation can be divided into two overall objectives: the proposal of a general architecture for building mobile CARS with rich social data and, on top of it, the proposal of models and methods to evolve current mobile recommender system design based on using contextual information from users and their environment to incorporate proactivity into the recommendation process, along with an appropriate user experience. Thus, the specific objectives of this work can be summarized as follows: • To identify which elements are needed for designing mobile context-aware recommender systems with rich social data. This study should clarify which modules are necessary and how they have to be used in a general architecture. • To propose a novel architecture valuable for building mobile context-aware recommender systems capable of using contextual information from different social sources as well as different mobile devices. As a result of the previous study, this proposal should include a set of functionalities to handle this architecture in the use case analyzed. • To implement and test the proposed general architecture in a real scenario. It should be built taking advantage of current technologies in the area of recommender systems and mobile development. • To identify open challenges related to generating proactive recommendations in mobile context-aware recommender systems.. I will study the implications of incorporating. proactivity into these kinds of systems not only in terms of methodological aspects, but also user experience factors. • To propose a general model for proactivity in mobile context-aware recommender systems. It should be as general as possible in order to be used in current recommender systems wanting to incorporate proactivity. • To evaluate the impact and suitability of proactive recommendations in terms of user experience in these systems.. I will evaluate different ways of providing proactive. recommendations to mobile users so as to study the impact of proactivity in their user.

(26) CHAPTER 1. INTRODUCTION. 4. experience, as well as analyzing the most important factors to consider when recommending proactively. • To provide general methods for incorporating proactivity into mobile context-aware recommender systems. As a result of the previous evaluation, I will design novel methods to incorporate proactivity following the general model proposed. • To validate the feasibility of the research approach. In order to assess these contributions the objectives described in this document should be validated in different real scenarios to demonstrate the suitability of the model and methods proposed.. 1.2. Research Methodology. The implementation of proactivity features in mobile CARS presents significant research challenges that involve several steps not only in terms of designing new artifacts (e.g. models), but also with the aim of developing technology-based solutions that have to be evaluated to demonstrate their validity in order to become real research contributions. In this spirit, this dissertation follows a design science approach. To be more precise, it satisfies the seven guidelines for design science in information systems research provided by Hevner et al. [Hevner 2004] as follows: 1. Design as an Artifact. This work contains the description of several artifacts that were created: a general architecture for mobile CARS, as well as models, algorithms and user experience methods for incorporating proactivity. 2. Problem Relevance. The relevance of the investigated problem is demonstrated through the presence of a vast array of related work in this field, in addition to significant business problems detected. 3. Design Evaluation. In order to show the functionality and utility of the approach presented, suitable evaluation methods are described and applied such that a discussion of the developed solution can be provided. 4. Research Contributions. Novel contributions that have been validated in several research projects are generated within this work (refer to section 8.4)..

(27) 1.2. RESEARCH METHODOLOGY. 5. 5. Research Rigor. This approach is designed based on related work and empirical evidence. Furthermore it is evaluated through rigorous statistical methods and quality measures derived from the quantitative studies carried out. 6. Design as a Search Process. The review of work behind this dissertation to understand the problem of incorporating proactivity into mobile CARS addresses the guideline that interprets design science as a research process for finding appropriate solutions. For that reason, this dissertation provides descriptions and evaluations of the most valuable artifacts generated during this work to solve the problems set out. 7. Communication of Research. The major contributions contained in this work have been published in the academic community (refer to section 7.3). As a result of this research methodology, the work carried out in the Perdidos en la Gran Ciudadi and GLOBAL excursionii projects has helped me to identify these challenges. The research associated to this dissertation has been partially supported by these projects in which building a recommender system was a key requirement. During this dissertation, and especially in chapter 7, I will give details on how the work carried out in these projects helped me to design, implement, test and validate the contributions proposed. However, as these projects have taken on special importance in the research methodology I followed to make this dissertation, I will briefly explain now the details relate to it. I started from the main objectives explained in section 1.1 to carry out wide-ranging research into the architectures, technologies and interfaces related to building mobile CARS in scenarios with rich social data. Bearing in mind the conclusions of this initial study, I designed a general architecture capable of being used in different scenarios with a recommendation model independent of the technologies used on the client and server side. In addition to this, I designed a generic REST API to manage contextual information and request recommendations for mobile CARS following this architecture. The work carried out in the Perdidos en la Gran Ciudad project allowed me to implement and validate the previous architecture in a real banking scenario. As a result, the system involved i https://labs.bankinter.com/blogs/projects/archive/2009/12/01/Perdidos-en-la-Gran-Ciudad.aspx ii www.globalexcursion-project.eu.

(28) CHAPTER 1. INTRODUCTION. 6. strict requirements as regards the special conditions these scenarios present in relation to handling sensitive data from the banking entity. As a result, new challenges appeared such as managing huge amounts of banking data in a secure and private way or generating the recommendations in real time using information from real purchases from Bankinter clients, together with contextual information retrieved from the mobile clients and the social relationships established between the bank customers. Thus, new adaptations to the architecture were made in order to overcome the challenges that arose. In the process of developing the system, I had the opportunity to evaluate the ideas implemented and the project itself among Bankinter customers. A spiral model methodology was carried out to enhance the final system taking into consideration the feedback given by users using it at all times. The main outcome of this project was a mobile CARS capable of generating cross-domain recommendations about different places (e.g. shops, supermarkets or restaurants) to Bankinter clients. Interesting research questions arose as a consequence of the results achieved in this project as the idea of proactivity came up: recommending without an explicit user request being made. In other words, investigating how mobile CARS can analyze the current situation of a user to provide him/her with personalized recommendations in a proactive way. An extensive review of the literature was carried out related to proactivity in recommender systems. As a result, I got in contact with Dr. Wolfgang Woerndl to collaborate with him for three months in a research stay at the Group of Applied Informatics and Cooperative Systems of the Technical University of Munich, in Germany. During that time, I contributed to the definition of a new model for proactivity in mobile CARS, achieving a new paradigm of assessing the situation of users to make recommendations to them proactively. Moreover, I designed and developed a mobile application following the model created to evaluate novel methods of recommending proactively by attending to user experience criteria. By studying the impact and acceptance of these kinds of novel recommendation methods among users, I reached significant outcomes on how to design user interfaces for proactivity in mobile applications, as well as determining important factors related to decide whether a situation warrants a proactive recommendation. Further research was required for combining and extending the previous results into a more general recommendation model which incorporated proactivity into mobile CARS. Related to that.

(29) 1.3. STRUCTURE OF THIS DOCUMENT. 7. new model, I also designed a set of novel methods to assess the appropriateness of a situation to generate a proactive recommendation. A final validation was carried out throughout the GLOBAL excursion European project in which I participated when I returned from Munich. GLOBAL excursion aims to introducing e-infrastructures to educators and pupils. It provides scientists and teachers with a package of activities, materials and tools for enabling the integration of e-infrastructures into school curricula. The main access point is the GLOBAL Virtual Science Hub (ViSH). It contains a selection of e-Infrastructures, a collaborative content repository where scientists and teachers are able to exchange and establish collaborations, and a virtual excursion room, where pupils are able to experience real e-science applications. In this educational scenario I was responsible for integrating a mobile CARS in ViSH. By using the previously defined general architecture, but this time including the new proactive recommendation model and its related methods, I built the ViSH recommender system capable of recommending learning objects and peers to its users. Apart from this, the appropriateness of proactive recommendations for educators using the platform was evaluated among teachers and scientists by studying different situations involving several devices and user activities, as well as temporal or geographical factors. As a result of this study, a valuable user model was developed of use in applications in other educational scenarios as regards proactivity. The entirety of this work as a whole has provided the author with a wide scientific context and has set out his work on an international basis, which is the main reason for the author to apply for the Doctor International Seal of Quality.. 1.3 Structure of this Document Chapter 2: State of the Art In this chapter, an in-depth study of current background and related work is presented to give a general overview of recommender systems and their evolution in recent years. The study includes the current state of the art of recommendation paradigms, paying special attention to those based on using context-awareness information.. Additional studies related to the implications of. recommender systems in terms of human computer interaction, as well as their application in mobile systems and the novel research results related to incorporate proactivity are also detailed..

(30) 8. CHAPTER 1. INTRODUCTION. Chapter 3: An Architecture for Social Mobile CARS In this chapter, results of the design and implementation of a new architecture for building mobile CARS are presented. The general architecture and its REST API are described, to be then applied to the Perdidos en la Gran Ciudad project carried out in the context of the research collaboration between the Universidad Politécnica de Madrid and the Spanish bank Bankinter.. This. architecture allows personalized recommendations to be generated in scenarios with rich social data and context-aware information coming from mobile devices, making the recommendation model independent of the technologies chosen for both the client and server sides. Methodological and technical details on its application in the banking scenario to generate cross-domain recommendations as to places (e.g. restaurants or shops) are provided, as well as the results from the user evaluation carried out among Bankinter clients.. Chapter 4: A Model for Proactivity in Mobile CARS In this chapter, a model for proactivity in mobile CARS is presented attending to how the current situation of a user can be assessed to decide whether it deserves a proactive recommendation. The model proposes a general paradigm to be applied in existing recommender systems that only need to include the analysis of contextual information to evaluate the situation, the final item being an assessment process capable of supporting contextual and non-contextual methods. To validate the model proposed, details on its application to generate proactive recommendations in online e-learning authoring tools are also presented.. Chapter 5: Proactivity Impact in Mobile CARS User Experience In this chapter, the design and implementation of novel user interfaces to visualize proactive recommendations in mobile CARS are presented. An evaluation carried out among users using the application developed to look into the impact of proactive recommendations and the usability of these proactive user interfaces is detailed. As a result, significant outcomes regarding the factors that have to be considered when designing proactive mobile CARS in terms of user experience are explained..

(31) 1.3. STRUCTURE OF THIS DOCUMENT. 9. Chapter 6: Methods to Incorporate Proactivity into Mobile CARS In this chapter, a novel model for proactivity in mobile CARS is proposed keeping in mind the previous contributions. The methods related to this model for assessing the appropriateness of a situation in order to decide whether it warrants a proactive recommendation are also presented. Furthermore, as part of the GLOBAL excursion European project, a validation of the initial architecture for building mobile CARS including the new methods for incorporating proactivity into them is also described. How the methods were applied to that scenario to recommend learning objects and peers to users registered in the educational social network developed within the project are detailed. In addition, the results of the study among educators evaluating the appropriateness and importance of proactive recommendations in their daily teaching experience are also presented to show remarkable outcomes extracted from using this kind of system in educational scenarios. Chapter 7: Validation and Results In this chapter, general results from the different projects involved are presented along with the validation of every contribution. It also details the results of this dissertation from the point of view of scientific results as dissemination into the research community, apart from additional information on the activities associated with this dissertation. Chapter 8: Conclusions In this chapter, the author summarizes the main contributions presented in this dissertation and outlines several future research activities based on this work..

(32) 10. CHAPTER 1. INTRODUCTION.

(33) Chapter 2 State of the Art 2.1 Recommender Systems In our daily life it is often necessary to make choices without sufficient information or personal experience about the alternatives we have to buy or use something attending to our needs. Usually we rely on recommendations from other people (sometimes acquaintances or friends) either by word of mouth, reviews in media or general guides. Recommender systems try to do this automatically by assisting and augmenting this natural process providing "recommendations as inputs, which the system then aggregates and directs to appropriate recipients" [Resnick 1997]. Another broad definition defines recommender systems as "software tools and techniques providing suggestions for items to be of use to a user" [Ricci 2010a]. "Item" is the general term used to denote what the system recommends to users. Therefore these systems made recommendations to users when they request suggestions about items they may need. These recommendations usually focus on a specific type of items (e.g. books or movies).. But recent proposals, such as the one described in this dissertation, try to offer. cross-domain recommendations of items belonging to several categories or domains. Therefore, recommender systems are primarily directed towards individuals who lack sufficient personal experience or competence to evaluate the potentially overwhelming number of alternative items offered by web sites, mobile applications or e-commerce platforms. This section provides a general overview of Recommender Systems and give details about the main different types existing in the field.. 2.1.1 General Overview Recommender systems emerged as an independent research area in the mid-1990s with the first papers on collaborative filtering [Goldberg 1992], [Resnick 1994], [Shardanand 1995] because they proved to be powerful tools for helping people to find content, products or services based on.

(34) CHAPTER 2. STATE OF THE ART. 12 their needs.. These systems use analytic technologies to generate personalized recommendations which are offered as a ranked list of items. In performing this ranking, recommender systems try to predict what the most suitable items are, based on the user’s preferences and constraints. Whereas at the beginning the majority of research was limited to recommend movies and shops, since 2007 this research began to extend to other fields such as books, documents or music [Park 2012], being one of the most important catalysts the domain-independent property of collaborative filtering. This is not unexpected, as recommender systems have been traditionally applied to commercial usage because they can drive a significant increase in purchase volume and may further alter the mix of products users buy [Hosanagar 2012]. Consequently, the role played by these systems can be seen from two perspectives: service providers and users. Recommender System Functions for Service Providers When we think about service providers that use recommender systems we can refer to commercial platforms like Amazoni or Netflixii focused on recommending their products to end users (e.g. books and movies among others respectively), or even to social platforms like Facebookiii or Twitteriv (which recommend mainly new social connections) that make money in a more indirect way because their aim is to achieve more and more users because they are based on advertising business models. However, we can also think about non-commercial platforms that include recommender systems in their processes to help their users. Regardless of the type of service provider, the reasons to integrate recommender systems in their environments can be described by at least one or more of the following functions [Ricci 2011] • Increase the number of items sold. This is probably the most important function for commercial recommender systems. Because of it users discover additional items compared to those usually sold without any kind of recommendation technique. The same principle can be applied to non-commercial scenarios where there is no cost for the user when selecting an item. In general, we can say that from the service provider’s point of view, the i www.amazon.com ii www.netflix.com iii www.facebook.com iv www.twitter.com.

(35) 2.1. RECOMMENDER SYSTEMS. 13. primary goal of introducing a recommender system is to increase the conversion rate, i.e., the number of users that accept the recommendation and consume an item, compared to the number of simple visitors that just browse through the information. • Sell more diverse items. As I mentioned above, recommender systems can help to select items that might be hard to find without a precise recommendation among a huge number of items available. For instance, in a book recommender system like Amazon, it is useless to recommend only the best-sellers (as almost everyone knows them). The service provider needs to advertise users the entire catalogue, but the best way to do it without affording the risk of suggesting an inappropriate book for a user is using a recommender system to generate personalized recommendations to the right users. • Increase the user satisfaction. By helping users to find the items they need in terms of relevancy or accuracy, a recommender systems can increase the usage and likelihood that a recommendation will be accepted. Furthermore, if the user experience is good due to a properly designed human-computer interaction, they will also enjoy using the system. • Increase user fidelity. The longer the user interacts with the site, the more refined his/her user model becomes and therefore the more personalized the recommendations provided are. Consequently this can foster users to be loyal to the site. • Increase the personalization. As a consequence of all the previous functions, the system understands better what the user wants or needs, increasing that way the level of personalization not only in the recommendations generated, but also in the way the site treat him/her.. Recommender Systems Functions for Users From the end-user point of view, Herlocker et al. [Herlocker 2004] define a set of popular goals and tasks that a recommender system can assist in implementing. • Annotation in context. Given an existing situation, select only those items that are suitable to the user’s context. For instance, in a restaurant recommendation, consider the user’s location as context information to recommend only restaurants that are at distance by walking..

(36) CHAPTER 2. STATE OF THE ART. 14. • Find good items. Provide to the user with a recommendation on a list of the best items, among all the available in the system, ranked according to his/her preferences. • Find all good items. It consists of recommending all the items available in the system that are suitable or fit the user’s needs. • Recommend a sequence. It is based on recommending items in a sequential way, attending to the idea that they are suitable to be consumed in a specific order. For instance, if we think about a system focusing on recommending research papers given a topic, we can imagine a sequence of important papers that should be read in a certain order to make easier for the user to understand better the area under study. • Recommend a bundle. It is similar to the previous function, but considering that the order is not relevant. For example, a tourist recommendation about points of interest in a city that should be visited. • Recommend synergetic bundles. Items that in bundle have more utility for the user than recommended singularly. For example, fork, knife and spoon in a cutlery domain. • Just browsing.. Sometimes users visit sites when they do not have the necessity of. purchasing because they find it pleasant, or just for curiosity. In this situation, the task of the recommender is to help the user to browse the items that are more likely to be consumed by the user or are side with his/her scope of interest. • Credible recommendations. Users frequently do not automatically trust a recommender. It is typical that many of them look for recommendations that they know they like before starting to believe in the system (e.g. their favorite books in sites like Amazon). For that reason, users can change their profiles in order to test the recommender. An interesting task would be to let the users do it to test how the system behaves. In other cases, users will not trust the system due to a possible level of bias in the source of recommendations. Therefore, recommender systems have to offer trustworthiness by using suitable data to recommend. • Improve profile. Recommender system should be able of gathering feedback from users after they have been recommended to improve their profiles. Otherwise, it can only provide to a target user with the same recommendation that would be delivered to an "average" user..

(37) 2.1. RECOMMENDER SYSTEMS. 15. • Express self. Some users may not care about the recommendations at all. Rather, what it is important to them is that they are allowed to express their opinions and feelings. A recommender system can leverage that to encourage users in self-expression by providing them with feedback methods so as to retrieve information that will improve the quality of future recommendations. • Help others. Some users are happy to contribute with information (e.g. their evaluations of items) because they believe that the community and the recommender system itself benefit from their contribution. For example, in an e-learning platform, a teacher can help their colleagues by rating all the learning objects he/she uses to facilitate the community distinguishes between rich and poor pedagogical resources. • Influence others. In recommender systems, there are users whose main goal is to explicitly influence others to purchase or consume a set of specific items. Trying to avoid this practice, or at least mitigate it, is an important task in any recommender system.. 2.1.2 Data and Knowledge Sources Recommender systems are known for being complex information processing and filtering tools that take as input heterogeneous kinds of data in order to output recommendations. The data are primarily about the items to suggest and the user who will receive these recommendations. The sources of data can be very diverse attending to the scenario in which the recommender system is deployed or the area of knowledge related to it. In any case, a general simplified classification (illustrated in Figure 2.1) of data used in recommender systems refers to three kinds of objects that will be involved in the recommendation process: items, users and transactions.. Items Items are the elements recommended to users. They can be characterized by their value or utility for the user, as well as their complexity and attributes or features. The value of an item may be positive if the item is useful for him/her, or negative if its appropriateness determines that it is a wrong decision for the user when selecting it. Apart from this, the value of an item can be related to the cost of acquiring it, which includes not only a real monetary cost eventually paid but also the cognitive cost of searching it or deciding if accepting or rejecting it..

(38) CHAPTER 2. STATE OF THE ART. 16. Figure 2.1 : General relationships between items, users and transactions.. Complexity refers to the data representation behind the item. For example, in an e-learning platform where teachers receive recommendations on educational materials, the recommender system must take into account aspects like the structure of the object, the time-dependent importance of the item or its representation (maybe textual or graphically based). But, at the same time, the recommender system designer must understand that even if the user is not paying for being recommended, there is always a cognitive cost associated to searching and selecting items. Apart from this, an item can be described by a set of attributes or features that will be closely related to its complexity. In a simple case, the item could be represented by a single id, or following the previous learning example, when recommending educational material the topic (e.g. biology), the target level (e.g. elementary school) or the language in which the material is available (e.g. English) could be the set of features used to represent it..

(39) 2.1. RECOMMENDER SYSTEMS. 17. Users Users in a recommender system may have very diverse goals and characteristics based on the scenario of recommendation. Therefore a wide range of information about users can be exploited to increase the level of personalization in the recommendations provided. User data is said to constitute the user model [Fischer 2001], [Kobsa 2001]. The selection of what information is considered to build the model depends on the recommendation technique chosen, but as a general rule, the user model profiles the user, i.e. encodes his/her preferences, needs and behavior. Since no personalization is possible without a convenient user model, unless the recommendation is non-personalized (e.g. suggestions based on top items), the user model will always play a center role when designing a recommender system. Furthermore, taking into account the recent advances in social network analysis, a user model may include those relations between users so as to being able of utilizing that information to recommend items to users that were preferred by similar or trusted users, or even suggest similar people to the target user (e.g. a friend of a friend or someone with the same interests).. Transactions A transaction can be defined as recorded information between a user and the recommender systems, where they may involve in addition an item or other users (Figure 2.1). This information is generated during the human-computer interaction and can be useful for the recommendation algorithm used. For instance, a transaction log may contain a reference to the item selected by the user and a description of the context (e.g. user location) in which that particular situation took place. In addition to this, it may also include an explicit feedback provided by the user about the item, allowing the system to be adaptive. A good example of feedback is the ratings given by the user for a selected item. Ratings are the most common and popular form of transaction. They may be collected explicitly (e.g.. the user provides his/her opinion about an item) or implicitly (e.g.. the. recommender annotates if the user accepts or rejects the recommendation provided). According to the classification proposed by Schafer et al. [Schafer 2007], ratings can take on a variety of forms (the most common are presented in Figure 2.2):.

(40) CHAPTER 2. STATE OF THE ART. 18. Figure 2.2 : Examples of common used rating scales: unary (like), binary (thumbs up / thumbs down), scalar (five-star and slider).. • Unary ratings can indicate that a user has observed or purchased an item, or otherwise rated the item positively. The absence of rating indicates that we have no information relating the user to the item. A good example of this kind of rating has been popularized by Facebook through the "Like" button. • Binary ratings indicate that the user choices between good or bad when evaluating an item. YouTubei uses this to allow users rating videos positively ("I like this") or negatively ("I dislike this") with a visual representation of a thumbs up / thumbs down icons. • Scalar ratings can consist of either numerical ratings, such as the five-stars (Netfilx) or the slider (Origoii ) methods, as well as ordinal ratings such as "strongly agree, agree, neutral, disagree, strongly disagree" (i.e. Likert scale [Likert 1932]) where users are asked to select the term that best indicates their opinion as regards an item. In more recent studies, Sparling and Sen [Sparling 2011] evaluate the cost and benefits in terms of cognitive load and decision time associated with different rating scales, showing that users prefer the five-star scale overall, though the best option may depend on the type of item rated as well as the target users. Another form of user transaction consists of tags associated by users with the items existing in the system. Marinho et al. [Marinho 2011] survey how the social tagging process carried out by users in a recommender system can improve the suggestions provided. For instance, in MovieLensiii recommender systems tags represent how their users feel about a movie (e.g. "too i www.youtube.com ii www.origo.by iii www.movielens.org.

(41) 2.1. RECOMMENDER SYSTEMS. 19. Table 2.1 : Summary of main recommendation techniques. Recommendation Technique. Description. Collaborative filtering. The user is recommended items that people with similar tastes and preferences liked in the past.. Content-based. The user is recommended items similar to the ones the user preferred in the past.. Demographic. The user is recommended items based on his/her demographic user profile attending to the ratings given by users in the same niche.. Knowledge-based. The user is recommended items based on inferences about his/her needs and preferences.. Community/Social-based. The user is recommended items based on his/her acquaintances’ preferences and shared interests.. Hybrid. Combine two or more techniques to improve recommendations performance and deal with disadvantages suffered by using them standalone.. long" or "boring") or inform about its topic or category (e.g. "thriller").. 2.1.3 Recommendation Techniques The core functionality of a recommender system consists of identifying the useful items for the user among the existing ones. Therefore, it must predict the utility of some of them to decide by comparison which one is worth recommending. This prediction is not a straightforward step, and so different methods and algorithms exist to calculate it. As a result, several types of recommender systems have been proposed since the first collaborative filtering based systems appeared. The main ones are described in the following lines attending to the taxonomy provided by Burke [Burke 2007] that has become a classical way of distinguish between recommender systems and referring to them..

(42) CHAPTER 2. STATE OF THE ART. 20 Collaborative filtering. In 1994 Resnick et at. [Resnick 1994] proposed the first ideas related to providing collaborative filtering (CF) of Netnews. The simplest and original implementation of this approach recommends to the active user items that other users with similar tastes liked in the past [Schafer 2007]. In other words, these systems evaluate or filter items considering the opinions and rates of other users that are similar to the active user. The similarity in tastes between two users is calculated based on the similarity in the rating history of them. For instance, if Bob liked the "Iron Man" and "The Avengers" movies, and Alice liked "Iron Man", a collaborative system should predict that the "The Avengers" is also suitable for her. However, this technique has their own limitations [Adomavicius 2005c]. As described below, mostly of them are related to the cold start problem, i.e. serious degradation of recommendation quality when only a small number of purchasing records or ratings are available [Ahn 2008], [Lam 2008]: • New user problem. In a real scenario less simplified than the previous example, the system must first learn the user’s preferences from the ratings that he/she makes before being able of recommending new items. Therefore if the user has not provided enough information about his/her tastes, the recommendations cannot be generated, or at least, they cannot be done with suitable accuracy. • New item problem. New items are added regularly to recommender systems. CF rely solely on users’ preferences to generate recommendations. Thus, until new items are rated by a substantial number of users, the recommender would not be able to suggest them. • Sparsity. In any recommender system, the number of ratings already obtained is usually very small compared to the number of ratings needed to predict items’ utility. Effective prediction of ratings from a small number of examples is important. But despite that, it is still primary for these systems to reach a critical mass of users in order to avoid recommending only a small set of highly rated items. For example, in the movie recommendation scenario there may be many movies that have been rated only by few people. These movies would be recommended very rarely, even if those few users gave high ratings to them. Besides, for.

(43) 2.1. RECOMMENDER SYSTEMS. 21. the user whose tastes are unusual compared to the rest of the population there will not be any other users who are particularly similar, leading to poor recommendations. • Grey sheep . It refers to those users whose opinions do not consistently agree or disagree with any group of people and thus do not benefit from CF (sometimes called grey users). Similar to this, the Black sheep problem exists. It corresponds to users that are the opposite group, whose idiosyncratic tastes make recommendations nearly impossible. Although this is a failure of the recommender system, non-electronic recommenders also have great problems in these cases, and for that reason black sheep is an acceptable failure. Anyway, despite these problems CF is considered to be the most popular widely implemented technique in recommender systems and important applications can be found due to its use in platforms like Amazon [Linden 2003] or because of the leverage of initiatives such as the Netflix Prizei competition launched in October 2006 to improve prediction accuracy in CF algorithms. Moreover, new proposals to improve it are still appearing [Koren 2011]. Content-based These systems recommend similar items to a user based upon a description of the item and a profile of the user’s interests [Pazzani 2007]. The similarity of items is calculated based on features associated with the compared items and the old ones that the user liked. For example, if Bob has positively rated the "Iron Man" movie that belongs to the "action" genre, then the system can learn to recommend other movies from the same genre. However, this technique has some important issues that have to be considered when using it [Shardanand 1995], [Balabanović 1997]: • Limited content analysis. Content-based techniques are limited by the features that are explicitly associated with the objects recommended by the system. In order to have a sufficient set of features, the content must either be in a form that can be parsed automatically by computer (e.g. text), or the features should be assigned to items manually. This can be mitigated by new information retrieval techniques that may work well extracting features from text documents or some other domains like image processing, but i www.netflixprize.com.

(44) CHAPTER 2. STATE OF THE ART. 22. the problem can be important if the items to analyze are audio or video streams. Another problem with limited content analysis is that, if two different items are represented by the same set of features, they are indistinguishable, i.e. a common problem in keyword-based systems. • Over-specialization. As the system can only recommend items that score highly against a user’s profile, he/she is limited to be recommended items similar to those already rated. Following the films example, if Bob has no experience with "comedy" movies, no matter that the best "comedy" movie is available in the system, it would be never recommended to him as it is not similar to his profile. • New user problem. Like in the collaborative filtering case, the user has to rate a sufficient number of items before the content-based system can really understand the user’s preferences and present the user with reliable recommendations. As a consequence, a new user with few ratings would not be able to get accurate recommendations.. Demographic These systems recommend items based on the demographic profile of the user. They assume that different recommendations should be generated for different demographic niches or life styles [Pazzani 1999]. A good example is done in Web pages that redirect users to particular sites attending to their language, country or age, recommending that way products accordingly. But other demographic parameters less public can be considered as the gender, education level, etc. While these approaches have been quite popular in the marketing literature, there has been relatively little proper recommender system research into pure demographic systems, as this kind of information is usually utilized to improve other recommendation techniques, like the collaborative filtering [Vozalis 2006].. Knowledge-based Knowledge-based systems recommend items based on specific domain knowledge about how certain item features meet user’s needs and preferences and, ultimately, how the item is useful for the user. Several approaches have been done in this area using case-based [Bridge 2005] or.

(45) 2.1. RECOMMENDER SYSTEMS. 23. constraint-based [Felfernig 2011] solutions. The first uses a similarity function to estimate how much the user needs (problem description) match the recommendations (solutions of the problem). The second exploits predefined knowledge bases that contain explicit rules about how to relate customer requirements with item features. These systems tend to work better than other recommenders at the beginning of the deployment due to the initial knowledge store, but they must learn how the user behaviors to be useful in the long-term. For that reason, Knowledge-based systems have fewer problems in this regard because they do not rely on having historical data about a user’s preferences.. Community-based Community-based recommender systems suggest items based on the preferences of the user’s friends bearing in mind the epigram "Tell me who your friends are, and I will tell you who you are". Research has pointed out that people tend to rely more on recommendations from people they trust (friends) than on recommendations from similar but anonymous individuals [Sinha 2001]. This observation, combined with the growing popularity of open social networks is generating a rising interest in this kind of systems, also referred to social recommender systems [Victor 2011b], [Groh 2012] or trust-enhanced recommender systems [Victor 2011a]. For example, in a social network like Facebook it is common for a user to be recommended about contents or products that their acquaintances liked or have previously consumed. The recommendations generated this way are based on information coming from the user’s (online) trust network, i.e. a social network which expresses how much the members of the community trust each other. Therefore, trust-enhanced recommender systems use the knowledge originated from these social networks to generate more personalized recommendations: users receive recommendations for items rated highly by people in their web of trust (WOT) or event by people who are trusted by the WOT members through friend-of-a-friend trust relationships. The research in the area of social-trust data sources is still in its early phase and results about the systems performance are mixed when comparing them for example with traditional collaborative filtering approaches [Groh 2007]. However, these systems have proved to be useful to avoid or at least mitigate cold-start situations when there is insufficient information about the user’s preferences to compute similarity to other users. This dissertation will provide some.

(46) CHAPTER 2. STATE OF THE ART. 24. research and results that include valuable outcomes associated to use social-trust data in recommender systems.. Hybrid Hybrid recommender systems are those that combine two or more of the techniques described above to improve recommendations performance, usually to deal with the cold-start problem. By doing this, they try to combine the advantages from several techniques at the same time they avoid or fix their disadvantages. Burke [Burke 2002], [Burke 2007] identifies seven different types: • Weighted. The score of different recommendation components are combined numerically to produce a single prediction of similarity [Mobasher 2004]. • Switching. The system uses several recommenders (usually ordered by its accuracy or personalization level) and chooses among recommendation components to apply the best one taking into account the situation [Billsus 2000]. • Mixed. Recommendations from different recommenders (every of them working better in a different situation) are merged and presented together to the user [Smyth 2000]. • Feature combination. Features derived from different knowledge sources are combined together and given to a single recommendation algorithm [Basu 1998]. • Feature augmentation. One recommendation technique is used to compute a feature or set of features, which is then part of the input to the next technique that uses it as a general item model [Melville 2002]. • Cascade. Recommenders are given strict priority, with the lower priority ones breaking ties in the scoring of the higher ones [Burke 2002]. • Meta-level. One recommendation technique is applied and produces some sort of model (e.g. user or item model), which is then the input used by the next technique [Pazzani 1999]. A typical example of hybrid system combines collaborative filtering and content-based techniques. The first ones suffer from new item problem (i.e. they are not able to recommend.