An architecture and functional description to integrate social behaviour knowledge into group recommender systems

Abstract

In this paper we consider the research challenges of generating a set of recommendations that will satisfy a group of users with potentially competing interests. We re view different ways of combining the preferences of differ ent users and propose an approach that takes into account the social behaviour within a group. Our method, named delegation-based prediction method, includes an analysis of the group characteristics, such as size, structure, personal ity of its members in conflict situations, and trust between group members. A key element in this paper is the use of social information available in the Web to make enhanced recommendations to groups. We propose a generic architec ture named ARISE (Architecture for Recommendations In cluding Social Elements) and describe, as a case study, our Facebook application HappyMovie: a group recommender system that is designed to provide assistance to a group of friends that might be selecting which movie to watch on a cinema outing. We evaluate the performance (compared with the real group decision) of different recommenders that use increasing levels of social behaviour knowledge.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEpu