Abstract: While many studies on typical recommender systems focus on making recommendations to individual users, social activities usually involve groups of users. Issues related to group recommendations are increasingly becoming hot research topics. Among the differences between individual and group recommender systems, the most significant 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. A new approach focuses on the impact of social factors on group recommender systemsâa computational model integrating the influences of personality, expertise factor, interpersonal relationships, and preference similarities. 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.
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