摘要
协同过滤算法研究正面临两大挑战:一是提高推荐系统的质量,尤其是高维稀疏数据系统的推荐质量;二是提高算法的可伸缩性。为了解决该问题,笔者提出了一个基于用户近邻和项目近邻的协同过滤改进算法。为了提高系统在线推荐性能,该算法分2步:1)线下的相似度计算和近邻计算;2)在线预测。通过对N个用户近邻和N个项目近邻的有效结合,该算法在线计算的空间复杂度为O(N)且具有较好的可伸缩性。实验表明,与经典的Pearson协同过滤算法相比,该算法不仅提高了推荐性能,而且也适用于高维稀疏数据系统。
Researchers of Collaborative Filtering（CF） algorithms are facing challenges with improving the quality of recommendations for users with sparse data and improving the scalability of the CF algorithms.To address these issues,an incremental CF algorithm based on N-nearest users and N-nearest items is proposed in this paper.To improve the online recommendation performance,the algorithm is divided into two steps： 1） The offline computation of similarities and neighbors;2） The online prediction.By using N-nearest users and N-nearest items in the prediction generation,the algorithm requires an O（N） space for the online prediction computation and at the same time gets improvement of scalability.Experiments suggest that the incremental algorithm provides better quality than the best available classic Pearson correlation-based CF algorithms when the data set is sparse.
出处
《沈阳师范大学学报（自然科学版）》
CAS
2012年第3期382-385,共4页
Journal of Shenyang Normal University:Natural Science Edition
基金
国家自然科学基金资助项目(11172060)
关键词
协同过滤
推荐系统
稀疏数据
collaborative filtering
recommender systems
data sparsity
