一种引入加权异构信息的改进协同过滤推荐算法

协同过滤作为当前应用最成功的推荐技术之一，其推荐质量在很大程度上取决于近邻用户选取的准确性，而数据的稀疏性问题（sparsity）和相似度度量方式（similarity metrics）严重影响着最近邻的选择。该文提出了一种引入加权异构信息的改进协同过滤算法。首先利用异构网络中丰富的语义信息和边属性信息，得到用户之间基于不同元路径的相似度；然后将相似度分别应用到典型的基于用户的协同过滤推荐算法中，得到基于每个相似度的用户评分值；最后采用监督学习算法为每个打分值分配不同的权重，融合为用户最终评分。在扩展MovieLens经典数据集上的实验结果表明，本文所提算法在精确度上较传统算法有显著提高。

An Improved Collaborative Filtering Recommendation Algorithm with Weighted Heterogeneous Information

Collaborative filtering is oneofthe most successful recommendation technologies, and the quality of collaborative filtering is determinedby the accuracy of the nearest neighbors. Data sparsity problem and similarity metricsseriously affect the choice of the nearest neighbors. Different from traditional recommendation tasks, in this paper, we propose an improved meta path-based collaborative filtering algorithm for weighted heterogeneous information networks. Firstly, we calculate the similarity among users based on different meta path by utilizing the rich semantic information and attribute information in weighted heterogeneous networks. Then we apply the similarity to user-based collaborative filtering algorithm and get multiple predicted rating scores based on different similarity. Finally we calculate the final predicted scores by combining various meta path information using supervised machine learning algorithms. The method is evaluated with the extended MovieLens dataset and experimental results show that our approach outperforms several traditional algorithms and make the result of recommendation more accurate in terms of accuracy.