协同过滤中有影响力近邻的选择

Influential Neighbor Selection in Collaborative Filtering

数据稀疏性制约着协同过滤的推荐性能,为此,首先根据用户评分数量定义了用户的影响因子,在计算用户之间的相似性时,增加了影响因子衡量用户关系;其次,根据用户评分质量定义了有影响力用户群体.在此基础上,结合用户的评分数量和评分质量,使选择的有影响力近邻最大程度上作用于推荐过程.实验结果表明,所提方法能显著提高推荐性能.

The recommendation performance of collaborative filtering is restricted by data sparsity. To solve this problem,the factor of user influence was thereafter defined according to the number of ratings to measure the relationship while calculating the similarity between users. Then,the influential user group was introduced according to the rating quality. On this basis,the chosen influential neighbor can work on the process of recommendations via combining the number of user ratings with the rating quality.Experiments show that the proposed method can significantly improve the recommendation performance.