Member contribution-based group recommender system

Abstract

Developing group recommender systems (GRSs) is a vital requirement in many online service systems to provide recommendations in contexts in which a group of users are involved. Unfortunately, GRSs cannot be effectively supported using traditional individual recommendation techniques because it needs new models to reach an agreement to satisfy all the members of this group, given their conflicting preferences. Our goal is to generate recommendations by taking each group member's contribution into account through weighting members according to their degrees of importance. To achieve this goal, we first propose a member contribution score (MCS) model, which employs the separable non-negative matrix factorization technique on a group rating matrix, to analyze the degree of importance of each member. A Manhattan distance-based local average rating (MLA) model is then developed to refine predictions by addressing the fat tail problem. By integrating the MCS and MLA models, a member contribution-based group recommendation (MC-GR) approach is developed. Experiments show that our MC-GR approach achieves a significant improvement in the performance of group recommendations. Lastly, using the MC-GR approach, we develop a group recommender system called GroTo that can effectively recommend activities to web-based tourist groups.