一种新的社会化相似度计算方法

基于社交网络的推荐算法引入社交网络信息到协同过滤算法中来, 使得用户朋友的偏好能够影响用户本身的偏好 。这些算法需要用到用户之间的相似度信息。目前有两个流行的基于共同评分项目集的相似度计算函数（VSS、PCC）。在很多情况下, 由于用户间没有共同评分项目集, 故无法计算他们之间的相似度。为了解决这个问题, 提出了一种基于矩阵分解的新的社会化相似度计算方法。在真实的包含社交网络的数据集上进行实验验证, 该方法的性能优于几个经典的基于社交网络的协同过滤算法, 而且能够解决新用户的冷启动问题。

New social similarities for collaborative filtering

Some recommendation methods based on social network have been raised. These approaches incorporate social network into the CF algorithms and users' preferences can be influenced by the favors of their friends. These social approaches require the knowledge of similarities among friends. There are two popular similarity functions:vector space similarity(VSS)and Pearson correlation coefficient(PCC). However, both friends similarity functions are based on the item-sets they rated in common. In most cases, these functions are impractical, i. e. if two friends do not share the same items in common, the similarity between them will be zeros. To solve this problem, this paper proposed a new social similarity(NSS) function based on the matrix factorization technique. It conducted experiment on a large dataset:Epinions, which was a widely-used dataset with social information. The experiment results illustrate that this approach outperforms the baseline models and achieves a better performance than social-based method, and can solve the problem of new users.