Group Recommendation Using Topic Identification in Social Networks

Many recommendation systems recommend item (book, music, news, or restaurant) to a group of users with the help of social network services like micro-blogging, which are called as group recommendation systems. However, as the social network services always contain large amounts of information on different topics, and people have different influence on different topic, so the group recommendation systems should take the topic identification in large-scale social networks into account to get an appropriate recommendation. In this paper, we proposed a new group recommendation method, which combines topic identification and social networks for group recommendation. In detail, we firstly identify different topical sub-groups by topics in social networks. Secondly, different user factors are used to calculate the user influence (including individual and social) on the topical sub-groups, which can depict the topical sub-group characteristics in different points of view. Experimental results demonstrate that the proposed method can improve the prediction accuracy of the group recommendation.