重庆邮电大学学报（自然科学版）2017，Vol.29Issue(4)：521-526,6.DOI:10.3979/j.issn.1673-825X.2017.04.015
基于K-medoids项目聚类的协同过滤推荐算法
Collaborative filtering recommendation algorithm based on K-medoids item clustering
摘要
Abstract
In general, traditional collaborative filtering recommendation algorithms do the prediction computation based on the whole rating matrix, which leads to the low efficiency.To remedy this weakness, a collaborative filtering recommendation algorithm based on K-medoids item clustering is proposed.The proposed algorithm clustered the items according to the item category attributes, and then constructed the user preference domain.Only the rating matrix in the user preferences domain is used to calculate the user similarity and generates the nearest neighbor set of the target user and recommendation results.Different from the other K-means based clustering methods, the present K-medoids based clustering method focuses on the item category attributes, which overcomes the low reliability problem of using user ratings.Moreover, the present clustering method has better robustness.Experimental results show that the proposed algorithm improves the recommendation quality.关键词
协同过滤/K-medoids聚类/用户偏好/推荐算法Key words
collaborative filtering/K-medoids clustering/user preference/recommendation algorithm分类
信息技术与安全科学引用本文复制引用
王永,万潇逸,陶娅芝,张璞..基于K-medoids项目聚类的协同过滤推荐算法[J].重庆邮电大学学报（自然科学版）,2017,29(4):521-526,6.基金项目
国家自然科学基金(61502066) （61502066）
重庆市前沿与应用基础研究(一般)项目(cstc2015jcyjA40025) （一般）
重庆市社会科学规划管理项目(2015SKZ09)The National Science Foundation of China (61502066) （2015SKZ09）
The Chongqing frontier and applied basic research (general) (cstc2015jcyjA40025) （general）
The Social Science Planning Fund Program (2015SKZ09) （2015SKZ09）
