基于社交网络的上下文感知推荐算法

Context-Aware Recommendation Algorithm Based on Social Network

上下文和社交网络信息对于构建精确的推荐系统是很有价值的。然而,传统的推荐系统还不能有效结合不同类型的上下文信息及社交网络信息来进一步提高推荐质量。为此,提出基于社交网络的上下文感知推荐算法SCRA (Social Network Based Context-Aware Recommendation Algorithm),对于不同类型的上下文,通过引入随机决策树的方式分割初始用户项目评分矩阵,在树的叶子结点应用矩阵分解,并结合社交网络信息引入了包含上下文信息的皮尔森相关系数来度量用户相似度,通过求解目标函数来预测潜在用户对项目的评分。在真实数据集上的实验表明该算法较传统推荐算法有着更高的准确率。

Context and social network information is very valuable for building accurate recommendation system. However, traditional recommendation systems could not combine different types of such information effectively to further improve the quality of recommendation. Therefore, we propose the context-aware recommendation algorithm based on social network SCRA (Social Network Based Context-Aware Recommendation Algorithm). For different types of context, we partition the rating matrix of initial user item by introducing random decision tree. In the leaf node of the tree, matrix factorization is used. Besides, we incorporate social network information by introducing Pearson Correlation Coefficient which contains context information to measure the similarity of users. To predict the rating of users for an item, we solve the objective function. Real datasets based experiments show that SCRA is better than the traditional recommendation algorithm in terms of precision.