基于覆盖约简的个性化协同过滤推荐方法

协同过滤(CF)无法同时提供高精度和多样化的个性化推荐.基于此情况,文中提出基于覆盖约简的协同过滤方法(CRCF).结合覆盖粗糙集中的覆盖约简算法与CF中的用户约简,匹配覆盖中的冗余元素与邻近用户中的冗余用户,利用覆盖约简算法将冗余用户从目标用户的邻近用户中移除,保证CF中邻近用户的高效性.在公开数据集上的实验表明,在稀疏数据环境下,CRCF可以同时为目标用户提供高精度和多样化的个性化推荐.

Personalized Collaborative Filtering Recommendation Approach Based on Covering Reduction

Collaborative filtering(CF) cannot provide personalized recommendation with both good accuracy and diversity. To address this problem, a covering reduction collaborative filtering(CRCF) is proposed in this paper. The covering reduction algorithm in covering based rough sets is combined with user reduction in CF, and redundant elements of covering are matched with redundant users of a neighbor. The redundant users are removed by covering reduction algorithm to ensure high effectiveness of the neighbor of a target user in CF. Experimental results on public datasets indicate that CRCF provides personalized recommendations for target users with both satisfactory accuracy and diversity in sparse data environment.