基于数据融合方法的群推荐系统研究

The Internet products have been increasingly diverse in recent years, which in some way enlarges the need for group recommendation. However, most applications of group recommendation technology are simply summing up the recommendation results of the members from the given groups. Considering the complexity of the relations among the group members, the precision of the recommendation and the satisfaction of group members would be more unpredictable and uncontrollable to improve than those of a single user. To address this problem, the former researches tend to attach importance to the similarity and the relations among the members. These researches have shown good results. However, they still fail to come up with a universal approach to this task under a diversity of circumstances. This paper provides a new data fusion algorithm focusing on group recommendation systems. The algorithm specifies the preference models among the members based on the similarity of their ranks of recommendations, then calculates the weights of the members to return the group recommendation lists. The algorithm is compared with some basic data fusion algorithms. Then the recall and F1 result upon these algorithms are tested.Finally we find that the algorithm we use in the paper show better results than the other algorithms.