Random walk models for top-N recommendation task  

作　　者：Yin ZHANG Jiang-qin WU Yue-ting ZHUANG 

机构地区：[1]School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2009年第7期927-936,共10页浙江大学学报（英文版）A辑（应用物理与工程）

基　　金：Project supported by the National Natural Science Foundation of China (Nos. 60525108 and 60533090);the National Hi-Tech Research and Development Program (863) of China (No. 2006AA010107);the Program for Changjiang Scholars and Innovative Research Team in University, China (No. IRT0652)

摘　　要：Recently there has been an increasing interest in applying random walk based methods to recommender systems. We employ a Gaussian random field to model the top-N recommendation task as a semi-supervised learning problem, taking into account the degree of each node on the user-item bipartite graph, and induce an effective absorbing random walk (ARW) algorithm for the top-N recommendation task. Our random walk approach directly generates the top-N recommendations for individuals, rather than predicting the ratings of the recommendations. Experimental results on the two real data sets show that our random walk algorithm significantly outperforms the state-of-the-art random walk based personalized ranking algorithm as well as the popular item-based collaborative filtering method.Recently there has been an increasing interest in applying random walk based methods to recommender systems. We employ a Gaussian random field to model the top-N recommendation task as a semi-supervised learning problem, taking into account the degree of each node on the user-item bipartite graph, and induce an effective absorbing random walk （ARW） algorithm for the top-N recommendation task. Our random walk approach directly generates the top-N recommendations for individuals, rather than predicting the ratings of the recommendations. Experimental results on the two real data sets show that our random walk algorithm significantly outperforms the state-of-the-art random walk based personalized ranking algorithm as well as the popular item-based collaborative filtering method.

关 键 词：Random walk Bipartite graph Top-N recommendation Semi-supervised learning 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论] V241.558[自动化与计算机技术—计算机科学与技术]