融合奇异性和扩散过程的协同过滤模型

Collaborative Filtering Model Fusing Singularity and Diffusion Process

作为解决信息过载问题的有效方式,推荐系统能够根据用户偏好对海量信息进行过滤,为用户提供个性化的推荐.但在推荐过程中,性能表现优异的协同过滤模型并没有充分利用上下文信息,这在一定程度上使系统面临性能瓶颈.为了进一步提高系统性能,从评分上下文信息着手,通过对项目评分进行分类统计获得评分奇异性,同时借鉴多渠道扩散相似性模型将推荐系统作为用户-项目二分网络的思想,提出了融合奇异性和扩散过程的协同过滤模型(collaborative filtering model fusing singularity and diffusion process,简称CFSDP).为了表明模型的优越性,比较实验基于MovieLens,NetFlix和Jester这3个不同的数据集展开.实验结果表明,该模型不仅具有良好的扩展性,而且在合理的时间开销下,可以显著提高系统的预测和推荐质量.

As a key solution to the problem of information overload,the recommender system can filter a large deal of information according to user’s preference and provide personalized recommendations for the user.However,traditional collaborative filtering models with excellent performance haven’t made full use of the contextual information in the process of recommendation,which to some extent confronts the system with the performance bottleneck.In order to improve the system performance further,this paper starts with the contextual information on ratings,and proposes a collaborative filtering model fusing singularity and diffusion process（CFSDP） by taking advantage of ratings’ singularities obtained from the classified statistics of ratings and referring to the similarity model of multi-channel diffusion which regards recommender system as a user-item bipartite network.To demonstrate the superiority of the proposed model,the study provides comparative experimental results based on the MovieLens,NetFlix and Jester data sets.Finally,the results show that the model not only has better extensibility,but also can observably improve the prediction and recommendation quality of system with a reasonable time cost.