Development of a group recommender application in a Social Network

Abstract

In today's society, recommendations are becoming increasingly important. With the advent of the Social Web and the growing popularity of Social Networks, where users explicitly provide personal information and interact with others and the system, it is becoming clear that the key for the success of recommendations is to develop new strategies which focus on social recommendations leveraged by these new sources of knowledge. In our work, we focus on group recommender systems. These systems traditionally suffer from a number of shortcomings that hamper their effectiveness. In this paper we continue our research, that focuses on improving the overall quality of group recommendations through the addition of social knowledge to existing recommendation strategies. To do so, we use the information stored in Social Networks to elicit social factors following two approaches: the cognitive modeling approach, that studies how people's way of thinking predisposes their actions; and the social approach, that studies how people's relationships predispose their actions. We show the value of using models of social cognition extracted from Social Networks in group recommender systems through the instantiation of our model into a real-life Facebook movie recommender application.

Group Recommendation Systems Based on External Social-Trust Networks

“…Though some papers [4][5][6][7]9] considered the social factors such as personality, trust, personal expertise, and social status of the members; once the information was acquired, the steps of the social networking group recommendation systems were as follows: At first, fetching the list of preference of all group members by individual recommendation system; then, increasing or decreasing the weight of a group of members of the article via social factors; finally, getting the ultimate results of group recommendation systems by some aggregation strategies, which consider the individual recommendation and weight. These methods utilize the social factors more or less; group members' weight is considered in ones; it means that a few of members would be ignored because of some group members play a decisive role, leading to the tendency to influence influential members.…”

“…The current social network group recommendation systems consider both the strength of the relationship between the members of the group into account [4][5][6] and the influence of social network information on each group members [7][8][9] and finally generate group recommenders through aggregation strategies. At present, the main influence of social network is the social impact of the group members on the group recommendation systems.…”

Incorporating group recommendations to recommender systems: Alternatives and performance

“…Quijano-Sánchez, Díaz-Agudo, and Recio-García (2014) improve the overall quality of group recommendations through the addition of social knowledge to existing recommendation strategies. They use the information stored in social networks to elicit social factors following two approaches: the cognitive modeling approach and the social approach.…”

A Case-Based Solution to the Cold-Start Problem in Group Recommenders

“…The details of the personality test are in [15]. In a real application, such as the Facebook social group recommender that we have built [9], trust between users u and v (u ∈ U, v ∈ U, u = v), t u,v , can be based on distance in the social network, the number of friends in common, relationship duration, and so on.…”

“…We have built a social group recommender as a Facebook application [9]. But, at the time of writing, it cannot provide the volume of data that we need for conducting experiments.…”