融合社交网络信息的协同过滤推荐算法*

在推荐系统中，协同过滤推荐算法往往面临数据集的高度稀疏性和推荐精度有限的问题.为了解决上述问题，在基于物品的协同过滤推荐框架下，分别在物品相似度的计算和用户对物品的评分预测阶段，利用社交网络中朋友关系信息选择性地填充评分矩阵中的缺失值，最大化利用评分矩阵中的已有信息，提出融合社交网络信息的协同过滤推荐算法.最后，在Epinions数据集上的实验表明，文中算法在一定程度上缓解数据稀疏性问题，同时在评分误差和分类准确率两个指标上优于其它协同过滤算法.

Collaborative Filtering Recommendation Algorithm Incorporating Social Network Information

To solve the problems of high data sparsity and limited recommendation precision of collaborative filtering recommendation algorithms, a collaborative filtering algorithm incorporating social network information is proposed under the framework of item-based collaborative filtering recommendation. In item similarity calculation period and user rating prediction period, social network information is utilized to fill missing values in rating matrix selectively and thus the existing rating information is utilized as much as possible. Finally, experiment is conducted on Epinions dataset. Results show that the proposed algorithm alleviates the data sparsity problem and outperforms other collaborative filtering algorithms on rating error and precision.