点击次数：

论文类型：期刊论文

发表时间：2018-10-01

发表刊物：WIRELESS PERSONAL COMMUNICATIONS

收录刊物：SCIE

卷号：102

期号：4

页面范围：3121-3140

ISSN号：0929-6212

关键字：Social tagging; Neighborhood-aware; Unified probability matrix factorization; Recommendation algorithm

摘要：Recommendation systems require sufficient information to provide proper recommendations. Both rating and tagging information can be used in social tagging systems. Many recommendation systems consider the relationships between users, items and tags, which affect the recommendation results. To address this issue, this paper proposes a neighborhood-aware unified probabilistic matrix factorization recommendation model that fuses social tagging. In the proposed approach, the similarities between users and items are first calculated by using tags to make neighborhood selections. Then, a user-item rating matrix, a user-tag tagging matrix, an item-tag correlation matrix and a unified probabilistic matrix factorization are constructed to obtain the latent feature vectors of three matrices to be recommended to users by optimizing the training parameters. In the experiments, the proposed model is compared with three other collaborative filtering approaches on the MovieLens dataset to evaluate its performance. The experimental results demonstrate that the proposed model uses the tag semantics effectively and improves the recommendation quality.