A Social Influence Approach for Group User Modeling in Group Recommendation Systems

While many studies on typical recommender systems focus on making recommendations to individual users, many social activities involve groups of users. Issues related to group recommendations are increasingly becoming hot research topics. Among differences between individual and group recommender systems, the most significant one is social factors of group users. Social factors, including personality, expertise factor, interpersonal relationships, and preference similarities, widen the gap between group and individual recommendations. Here, a new approach focusing on the impact of social factors on group recommender systems is proposed. A computational model integrating the influences of personality, expertise factor, interpersonal relationships, and preference similarities is described in detail. Comparative experiments are conducted on two datasets. The experimental results show that the proposed approach can provide more accurate and satisfactory group recommendations, especially when social influences are significant.