SNS背景下基于Tag和Rating相似度融合的协同过滤

摘要

The emergence of social networking services (SNS) provides an opportunity for the application of tag. In this paper, we choose the website based on SNS as the background, and then integrate tag information into collaborative filtering recommendation system. So we proposed collaborative filtering recommendation system based on similarity fusion of tag and rating under the background of SNS. It can help us to reduce the influence of data sparsity on the recommendation accuracy. First, we calculate the user similarity based on Tag information and Rating information respectively, and then get the integrated similarity by the fusion of these two similarities. Finally, Collaborative Filtering can be executed based on this integrated similarity. Experimental results show that the proposed algorithm can improve the accuracy of the recommended.%SNS即社会性网络服务的出现为Tag技术的应用提供契机,以基于SNS的网站为背景,将Tag信息作为补充信息融入协同过滤推荐系统,提出了SNS背景下基于Tag和Rating相似度融合的协同过滤,以降低数据稀疏性对推荐精度的影响.首先分别计算基于Tag信息和Rating评分信息的用户相似度,然后将这两种相似度进行融合得到综合相似度,最后据此进行协同过滤推荐.实验结果表明本文提出的算法能提高推荐的精度.