Problemas textuales

We have conducted all our experiments with publicly available datasets. The ap- proach uses implementations which are partly deployed in an existing, publicly platform (Tuffy), and the rest is made accessible online9.

To facilitate further reproducibility of the experiments, we are also making avail- able the MLN programs together with the designed schemas and query files for each of the datasets. The parameter setting used for the evaluations are explained above in Sec.6.8.5and Sec.6.8.7.

6.9

Summary

In this section, we presented a novel adaptive approach, METIS, to address the cross-domain recommendation problem. There are two crucial components of this approach. The first component is the model introduced for capturing domain knowledge and the relations between users and object. To the best of our knowl- edge, this is the first work applying markov logic for the task of recommendation, particularly in a hybrid approach. The proposed theoretical model is generic and allows to model any domain of interest.

The second important component is the mechanism for transferring first-order probabilistic knowledge learned in one domain to another target domain, where user preferences are very sparse. The goal is then to exploit the transferred knowl- edge in order to enable prediction of user preferences in the target domain. This approach is first of all helpful to comprehend the logically-expressed dependency rules, and therefore explain the reasoning behind the performed predictions. For example, we can gain better insights that dependencies on the age of the users play a greater role in predicting the rating behavior than the country where the user come from.

Besides an increased comprehensibility, the approach is able to make accurate pre- dictions by outperforming some of the most popular techniques in recommender systems. It achieves higher accuracy for the single-domain cases, and for particular settings of cross-domain cases. The significance of the approach lies in its ability to deal with very sparse information of user preferences. Even when trained on much smaller datasets of ratings than other state-of-the-art cross-domain CF methods, it is still able to make recommendations with similar or even smaller prediction errors than these methods.

Part III

Conclusions

C

7

Conclusions and Outlook

We conclude this thesis by summing up the main hypothesis, the significance of the achieved results and conclusions drawn thereof. Finally, we provide an outlook on further research directions for future work.

7.1

Conclusions

The motivation for this thesis was driven by the latest developments on the Web, where people have to continously cope with large amount of information that is increasing daily. For humans, it is impossible to survey this information manually in order to find the relevant resources. Automated methods, such as recommender systems, are useful to facilitate the information seeking process by considering user preferential behavior expressed in terms of the navigation on the Web or explicit ratings. Yet, the information online is highly heterogeneous and the interest of the users span across different types of content and various domains. Based on this motivation, we raised the following main hypothesis in this thesis:

Accurate cross-domain user recommendations can be generated through pre- dictive techniques, which leverage semantically-enriched models of user Web behavior.

The hypothesis was sustained with four research questions that were investigated and led to four significant contributions of the thesis.

The first contribution is an approach that formalizes user browsing behavior in an open Web setting. A significant result of this approach is an ontological model that can be reused by researchers and industry for user Web behavior modeling, elim- inating ambiguity and facilitating data exchange. We also show that it is possible to enrich the information on user behavior by harvesting the knowledge available today on the Web in a structured form, which can also be easily processed by ma- chines. We provide a set of techniques that enable such semantic enrichment. For the cases when such data is not available, we present a method to learn semantic types of Web resources via training with existing examples. This combination al- lows the overall approach to take advantages of both automated enrichment and machine learning solutions. The semantic formalization of user browsing behavior lays the foundation for effective techniques of behavior pattern analysis. We show that dynamic patterns of user browsing behavior can be discovered if we enable reasoning with semantic and temporal conditions.

7.1. CONCLUSIONS

The second contribution is a framework that is able to generate accurate recom- mendations of resources across domains. It exploits the implicit user preferences captured in the browsing behavior and the enriched semantics of Web resources harvested in the first contribution. This work is highly significant to present the value of adopting Semantic Web technologies in recommender systems that deal with heterogeneous information spread across different domains. The importance of our contribution lies in the stable, yet flexible methodology for establishing cross-domain bridges among the separate, diverse domains on the Web.

Our third contribution is an expressive theoretic model for making recommenda- tions that allows us to reason about many different relations at the same time. Based on Markov logic, which is a simple and powerful language that combines first-order logic and probabilistic graphical models, the model is able to offer both expressiv- ity and uncertainty handling. The proposed theoretical model goes beyond tradi- tional ways of expressing user-object preference dependencies in a flat representa- tion, focusing solely on dyadic relations. We can express various relationships and dependencies, and moreover, this is done through rules that are comprehensible to humans. This is of significant value if compared to other approaches that generally rely on unidentifiable clusters, or prediction models of the black-box type where it is difficult to explain the reasoning behind the recommender. Our model is generic and allows to capture various domains of interest. It also allows the flexibility of expressing both content-based and collaborative filtering features. Most impor- tantly, we show that user preference prediction with our approach is very precise and outperforms traditional approaches with respect to accuracy measures. The fourth contribution of our work is an adaptive cross-domain recommendation approach that deals with the cases when user preference data for resources is very sparse. In this setting, we consider explicit preference feedback of users, such as in the form of ratings. We extend the expressive relational model of user-object preferences, provided as part of the third contribution, to build a novel approach for knowledge transfer from one source domain to another sparse domain. The ap- proach comprises a mechanism for generating accurate recommendations to users in a target domain that is unknown to them. We show that this approach is feasi- ble, and based on various configurations (e.g. recommendation rules filtered out in the target domain) it leads to very accurate prediction of ratings. The signif-

icance of this contribution lies particularly in the ability to deal with extremely sparse datasets of user preferences, such as in cold-start scenarios where the con- tent provider has very few or almost no knowledge on user behavior.