摘要： 在协同过滤技术的实际应用中，提出一种数据迁移和聚类相结合的方法来解决新系统冷启动问题。采用斯皮尔曼秩相关公式度量用户之间的相似度，使用期望最大化聚类算法对原数据集用户进行聚类。对于不同的簇，选取平均打分最高的N个项目作为推荐内容，针对目标数据集的用户，计算用户所属的簇以及对簇的隶属度，按照隶属度比例给用户推荐其所属簇的推荐列表。与TAM算法和CF算法的实验对比结果表明，该算法在解决新系统冷启动问题方面有较好的效果。

关键词: 推荐系统, 冷启动, 协同过滤, 数据迁移, 迁移学习, 期望最大化

Abstract: In the practical application of collabrative filtering technology, this paper proposes a method which combines the transfer learning technology and clustering technology to solve the cold start problem of new system. This method uses spearman rank correlation to measure the similarity between two users, and takes use of expectation maximization algorithm to cluster the users of source dataset into several clusters. For different cluster, N items who have the higher average score are selected as this cluster’s recommendation list. For users of target dataset, calculate the clusters belong to the users and membership of the clusters. It recommends the recommended list of the cluster according to the membership in proportion. Experimental results show that the algorithm is more available to solve the cold start problem of new system than the TAM algorithm and CR algorithm.

Key words: recommender system, cold start, collaborative filtering, data transfer, transfer learning, expectation maximization

中图分类号: 

• TP181