基于多权重相似度的随机漫步推荐算法

在传统个性化推荐算法的基础上，提出了一种基于多权重相似度的随机漫步推荐算法。为了解决传统协同过滤算法中忽略了社交网络、热门项目以及共同评分项目之间影响等问题，通过引入万有引力公式计算社交网络中的用户相似度，并对传统协同过滤算法中的相似度进行改进，采用权重因子结合这两者相似度，最后开拓性地结合随机漫步算法进行商品推荐。实验结果表明，提出的算法具有比其他推荐算法更好的推荐性能。

Research on random walk recommendation algorithms based on multi-weight similarity

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.