关键词: 局部影响力, 全局影响力, 随机游走模型, 社会化推荐, 协同过滤

Abstract: Social recommendation alleviates the data sparse problem in recommendation to some extent, while it usually only involves the local influence between neighbors. Taking full account of local and global influence, this paper proposes a social recommendation algorithm based on a user influence walk model. The algorithm first calculates the local influence based on neighbors’ trust relations and users’ historic behaviors, and explores the global influence by measuring users’ quality of rating. Then, exploit local and global influence together to compute the transition probability between each node in the random walk model. A lot of experiments is done based on RMSE（Root Mean Squared Error）, coverage rate and F-Measure, the results show that the proposed algorithm improves performance for recommendation in some degree.

Key words: local influence, global influence, random walk model, social recommendation, collaborative filtering