MyMovieHistory: Social Recommender System by Discovering Social Affinities Among Users

Abstract

Social network information has recently been used for the improvement of the performances of recommender systems with regard to both individual users and groups. During the selection of the items for a group, the role of the corresponding relationships (e.g., position, dependency, and the strength of the social ties) is often more important than the individual preferences; however, the existing works do not sufficiently consider this important factor for group recommendations. We therefore propose a novel recommendation method that is based on a social affinity between the common histories of users. The proposed method consists of an intermovie similarity calculation that is based on weighted features for the generation of an initial social-affinity graph, and the subsequent computation of a user's affinity to a group that is based on the graph. To apply the method for a service, we developed a "MyMovieHistory" application for the Facebook social media platform, and the synthetic dataset results of the experiment show that our proposed method can discover social affinities in an efficient manner.