El reposo en los escolares: la siesta y el descanso nocturno

Many researchers have discussed the effect of social networks as recommendation approach and compared the recommender performance with other types of recommendation systems. A study presented by Bonhard discussed the effect of relationships between advice-seeker and recommender and incorporated social networking features within the recommendation process. The study examined the effect of knowing the recommender on an advice-seeker selections and the effect of knowing the advice- seeker on the recommendations of the recommender and how this in turn helped them to make decisions, and hence, improve the recommendation process. The study applied considered group of participants that knew each other and were asked to provide basic profile information and rate 20-30 films and then to rate some profiles in terms of familiarity, similarity and trust. The results showed that an advice-seeker selected programmes that were recommended (rated) by known persons faster than without knowing them. In addition to knowing the recommender, two conditions have been recognized that affected decision making. The first one was when the advice-seeker knows that the recommender has similar tastes and secondly when both have mutual knowledge about each other’s tastes. Even when they are different in tastes, the recommender will know what the advice-seeker will like (Bonhard & Sasse, 2006). In the same field, Mao discussed how the social influence between friends could affect the decision-making of item selection. The author developed a new parameter learning algorithm based on expectation maximization. This is based on integrating user behaviour, social influence and item content in order to enhance the recommendation performance. The system has been evaluated through a set of experiments using datasets collected from different web sites. The results showed that social influence has a valuable enhancement on the recommendation process and users are more likely to prefer friends’ recommendations

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than those from other recommendation schemes but combining both user preferences and social influence is more effective than depending on social influence only (Ye, Liu, & Lee, 2012).

Another recommendation system has been proposed based on social networks related to items used in electronic commerce. This system relied on making recommendations for items which have been purchased or rated by members of different social networks. As an example, when someone purchases any item online, the system provides recommendations from trusted friends and members in the same social network and members in other networks. The ranking of those recommendations is based on the relationship proximity of the members with the target user. That means the system relies on friends followed by other members who have mutual friends with the target user within the same social network then the strangers (members within other social networks) will be at the end. This system consists of a social network module, recommendation engine and data repository as shown in figure 2.18.

The social network module is configured to connect the recommendation application to the social network through an API provided by that social network. The data repository contains information about items that should be recommended within the social network. The recommendation engine provides the recommendations based on the social networks relationship proximity that is extracted from the social network (Berman, Iyer, Richardson, Rahurkar, & Seetharaman, 2012). However, this system was applied to purchasing items and exploited only one aspect of social network information (relationship proximity) in ranking the recommended items.

A recommendation system based on social networks only has been proposed by Fischer. This system relied on sharing the users’ activities with friends who belong to one or more social network. The recommended items are weighted based on the closeness of the relationship with the target user which is determined based on the history of content sharing activity between users. This system comprises a group of services that are managed by a main application which communicates with the different web servers. Through these services, the application retrieves the needed data that is relevant to each user and their relationships with other users in each network (Fischer, 2012).

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Fig. 2.18 Block Diagram Describes the Idea of Social Network Recommendation System (Berman et al., 2012)

Another study proposed a social network recommender for an IPTV system where the recommendations for content had been determined based on watched items, likes or those that have been tagged by other social network members. This system could receive data about content items from other sources or rating engines such as IMDB or social networks such as Facebook. Figure 2.19 shows a block diagram for the designed social network recommender. The recommendation system includes a tracker module to track user interactions with content and to collect further information that may be relevant to provide recommendations to other users who are members of the same social network. This system can perform a keyword search to find content matching that keyword in the content database or user’s social network. This keyword could be a genre, actor name or words in

Social Network

Data Repository Web Server

Recommendation application Social Network Module

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the title or description belonging to the viewed programmes by the user themselves or members of their social network (Mathur, 2013). However, this system was applied to a limited content source which was available in the IPTV system and relied on the users’ activities on the social networks such as comments or tags.

Fig. 2.19 IPTV Social Network Recommendation System (Mathur, 2013)

Another proposal including created a web site for recommending films to users based on a trust relationships. The web site has its own social network which offers the users a list of friends who are in the network and to be able to rate those friends, based on the strength of the relationship that exists such as acquaintance, good friend or best friend. Moreover, the web site asked the users to rate as much as possible different films and allowed them to write reviews (opinion) about each film. Based on these explicit ratings for both movies and friends, the system recommends and ranks a list of movies to each user within their own account. Additionally, the system presents the film reviews of the most trustworthy people where the trust ratings of those reviewers are displayed to the user. The results obtained from user trials showed that the accuracy of predicting films based on the trust associated with social networks were better than those based on the simple traditional recommendation techniques (Golbeck, 2006).

Recommendation System User Device

Social Networking Site Head End Server

Content Information Source Content

Information

Recommendations Content

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