基于非负矩阵分解的群组推荐算法

近年来，随着媒介技术的快速发展，人们成组活动的现象逐渐增多，群组推荐系统也逐渐受到关注。现有的群组推荐系统往往将不同的成员视为同质对象，忽视了成员专业背景和项目固有属性之间的关系，无法真正地解决融合过程中的偏好冲突问题。为此，提出一种基于非负矩阵分解的群组推荐算法，通过非负矩阵分解将群组评分信息分解为用户矩阵和项目矩阵，针对2个矩阵分别利用隶属度和专业度权值计算得到项目隶属度矩阵和成员专业度矩阵，并由此获得各成员在不同项目上的贡献度来构建群组偏好模型。实验结果表明，所提算法在不同群组规模和组内相似度的情况下依然具有较高的推荐准确度。

A group recommendation algorithm based on non-negative matrix factorization

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.