基于社交网络的上下文感知推荐算法
Context-Aware Recommendation Algorithm Based on Social Network
作者: 陈 磊 , 李 贵 , 李征宇 , 韩子扬 , 孙 平 :沈阳建筑大学信息与控制工程学院,辽宁 沈阳;
关键词: 推荐系统; 上下文感知; 社交网络; 矩阵分解; Recommendation System; Context-Aware; Social Network; Matrix Factorization
摘要:Abstract: 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.
文章引用: 陈 磊 , 李 贵 , 李征宇 , 韩子扬 , 孙 平 (2015) 基于社交网络的上下文感知推荐算法。 软件工程与应用, 4, 101-113. doi: 10.12677/SEA.2015.45014
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