摘要
协同过滤推荐算法因其合理的可解释性以及简单的实现过程而被广泛应用。然而,在推荐系统中数据集通常具有规模大、稀疏度和维度高等特点,这些特点给协同过滤推荐算法带来了很大的挑战。为了缓解上述问题,提出了一种基于评分区域子空间的协同过滤推荐算法。基于用户-项目评分矩阵,该算法首先将评分范围划分为3个区域,即高评分区域、中评分区域以及低评分区域,根据这3个区域分别为每个用户寻找其项目子空间,即高评分子空间、中评分子空间以及低评分子空间。其次,定义了一种新的相似度计算方式,在各区域子空间中分别计算用户之间的评分支持度,只有当用户在各个子空间上的评分支持度都很高时,用户之间才是相似的。这种方式避免了惰性评分用户的评分干扰。实验结果表明,该算法能够在一定程度上解决数据稀疏性问题,特别是针对高维数据能降低其计算复杂度,并提高其推荐性能。
Collaborative filtering(CF)recommendation algorithm is widely used because of its reasonable interpretability and simple process.However,datasets in recommendation systems have the characteristics of large scale,high sparsity and high dimensionality,which bring a great challenge for CF recommendation algorithms.To alleviate the above issues,this paper proposes a collaborative filtering recommendation algorithm based on the rating region subspace(RRS).According to the user-item rating matrix,RRS firstly divides the scoring range into three different regions:high scoring region,medium scoring region and low scoring region.On the basis of these three regions,each user finds its item subspaces,that is,high rating subspace,medium rating subspace and low rating subspace.A new similarity measurement method is defined to calculate the rating support between users in each region subspace.Only if the rating supports of users in all subspaces are high,the users are similar,which avoids the ra-ting interference of lazy users.Experimental results show that the proposed method can solve the issue of data sparsity to a certain extent,reduce the computational complexity and improve the recommendation performance,especially on high-dimensional datasets.
作者
孙晓寒
张莉
SUN Xiao-han;ZHANG Li(School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China)
出处
《计算机科学》
CSCD
北大核心
2022年第7期50-56,共7页
Computer Science
基金
江苏省高校自然科学研究项目(19KJA550002)
江苏省六大人才高峰项目(XYDXX-054)
江苏高校优势学科建设工程资助项目。
关键词
项目子空间
评分支持度
协同过滤
稀疏性
高维性
Item subspace
Rating support
Collaborative filtering
Sparsity
High dimensionality
