关键词: 群组推荐系统, 偏好融合, 非负矩阵分解, 贡献度

Abstract: In recent years, with the rapid development of media technology, the phenomenon of peoples group activities has gradually increased, and the group recommendation system has gradually attracted attention. Existing group recommendation systems often treat different members as homogeneous objects, ignoring the relationship between members professional backgrounds and inherent attri- butes of items, and cannot really solve the problem of preference conflicts in the fusion process. Therefore, a group recommendation algorithm based on non-negative matrix factorization is proposed. The algorithm decomposes the group rating information into the user matrix and the item matrix by non- negative matrix factorization. According to the two matrices, the item membership matrix and member professionalism matrix are calculated by using membership and professionalism weights respectively, and the contribution degree of each member on different items is obtained to construct the group prefe- rence model. The experimental results show that the proposed algorithm still has high recommendation accuracy in the case of different group size and intra-group similarity.

Key words: group recommendation system, preference aggregation, non-negative matrix factorization, contribution degree