广西师范大学学报（自然科学版）2011，Vol.29Issue(1)：173-178,6.
基于随机游走和聚类平滑的协同过滤推荐算法
A Collaborative Filtering Algorithm Based on Random Walk and Cluster-based Smoothing
摘要
Abstract
Collaborative filtering has been widely used in E-Commerce recommendation systems,but the sparsity of data affects the quality of collaborative filtering recommendation. A two-stage collaborative filtering algorithm is proposed based on random walking and cluster-based smoothing. For off-line stage,calculate the correlation between items,suggest a new method which describes the correlation between items by cumulating weighted transition probability of each step. Cluster items according to the item correlation matrix,then smooth the unrated data by using clustering information. For on-line stage,search the target item's neighbors according to the correlation between items cumulated during the off-line and predict. This method can enhance the description of the correlation between items. The experiment resuits illustrate that searching neighbors according to the item correlation matrix will become more accurate,which can effectively relieve the impact of sparse data and improve the quality of recommendation.关键词
协同过滤/随机游走/相关性描述/聚类平滑/MAEKey words
collaborative filtering /random walk /correlation description /cluster-based smoothing/MAE分类
信息技术与安全科学引用本文复制引用
周军军,王明文,何世柱,石松..基于随机游走和聚类平滑的协同过滤推荐算法[J].广西师范大学学报（自然科学版）,2011,29(1):173-178,6.基金项目
国家自然科学基金资助项目(60963014,60663307) （60963014,60663307）
江西省自然科学基金资助项目(2007GZS0186) （2007GZS0186）
江西省教育厅科技项目(GJJ09365) （GJJ09365）
江西师范大学青年成长基金资助项目(2696) （2696）
