基于多权重相似度的随机漫步推荐算法.

Based on the traditional personalized recommendation algorithm, this work presented a random walk recommendation algorithm using multi-weight similarity. In order to make up the absence of the influence among social networks, popular items and common scoring items in traditional collaborative filtering algorithms, this paper introduced a gravity formula, which could calculate user similarity in social networks. In addition, this work used weight factors to combine the gravity formula method with the traditional collaborative filtering algorithms. Furthermore, it also added the random walk algorithm for recommendation functions. The experimental results show that the proposed algorithm has better recommendation performance by comparing with other kinds of recommendation algorithms. [ABSTRACT FROM AUTHOR]