State-of-the-Art in Group Recommendation and New Approaches for Automatic Identification of Groups

Abstract

AbstractRecommender systems are important tools that provide information items to users, by adapting to their characteristics and preferences. Usually items are recommended to individuals, but there are contexts in which people operate in groups. To support the recommendation process in social activities, group recommender systems were developed. Since different types of groups exist, group recommendation should adapt to them, managing heterogeneity of groups. This chapter will present a survey of the state-of-the-art in group recommendation, focusing on the type of group each system aims to. A new approach for group recommendation is also presented, able to adapt to technological constraints (e.g., bandwidth limitations), by automatically identifying groups of users with similar interests.KeywordsRoot Mean Square ErrorRecommender SystemCommunity DetectionCollaborative FilterTechnological ConstraintThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.