Enhanced Group Recommendation Method Based on Preference Aggregation

Group recommender systems have recently become one of the most prevalent topics in recommender systems. As an effective solution to the problem of group recommendation, Group recommender systems have been utilized in news, music, movies, food, and so forth through extending individual recommendation to group recommendation. The existing group recommender systems usually employ aggregating preference strategy or aggregating recommendation strategy, but the effectiveness of both two methods is not well solved yet, and they respectively have their own advantages and disadvantages. Aggregating preference strategy possesses a fairness problem between group members, whereas aggregating recommendation strategy pays less attention to the interaction between group members. This paper proposes an enhanced group recommendation method based on preference aggregation, incorporating simultaneously the advantages of the aforesaid two aggregation methods. Further, the paper demonstrates that group preference and personal preference are similar, which is also considered in the proposed method. Experimental results show that the proposed method outperforms baselines in terms of effectiveness based on Movielens dataset. © Copyright 2018, Institute of Software, the Chinese Academy of Sciences. All rights reserved.