摘要： 针对传统协同过滤算法在计算用户相似度过程中,由于数据稀疏性导致的无法计算、失真、虚高等问题,提出一种融合离散量和兴趣贴近度的相似度度量方法。收集用户对项目的评分数据,从全信息量角度进行分析,通过引入离散量相关理论进行用户评分向量间的相似度计算,对评分相似的用户进行初步筛选,利用用户兴趣贴近度对相似度结果进行进一步加权处理,得到融合用户兴趣偏好信息的相似度结果,以此为基础,采用协同过滤算法进行个性化推荐。实验结果表明,该算法可有效提高信息推荐系统的推荐质量,在数据极端稀疏的情况下也能保持较好的性能。

关键词: 协同过滤, 相似度计算, 数据稀疏性, 离散量, 推荐效果

Abstract: To solve the problems of the traditional collaborative filtering algorithm which in the process of users’ similarity calculation that due to data sparsity the traditional similarity could not be used in calculating the similarity and cause the information distortion and virtual height,this study proposes a new algorithm that combine the discrete contents with the interests appropinquity degree.Collects the users’ ratings at the beginning of this study,that analyzes the ratings from the aspect of all users’ information ratings and calculates the similarity between user rating vectors by introducing discrete contents theory,conducts a preliminary screening of the similar users,the similarity results are further weighted by the users’ interests appropinquity degree,in the final of this study the users’ interests information fuses in the similarity results,on this basis,the personalized recommendation that is proceeding well by using collaborative filtering algorithm.Experimental results show that the algorithm can significantly improve the quality of recommendation,at the same time,it can maintain well performance in the case of extreme data sparsity.

Key words: collaborative filtering, similarity calculation, data sparsity, discrete quantity, recommendation effect

中图分类号: 

• TP391