基于随机游走和聚类平滑的协同过滤推荐算法

广西师范大学学报（自然科学版） ›› 2011, Vol. 29 ›› Issue (1): 173-178.

基于随机游走和聚类平滑的协同过滤推荐算法

周军军, 王明文, 何世柱, 石松

江西师范大学计算机信息工程学院,江西南昌330022

收稿日期:2010-12-22 发布日期:2018-11-16
通讯作者: 王明文(1964—),男,江西南康人,江西师范大学教授,博导。E-mail: mwwang@jxnu.edu.cn
基金资助:
国家自然科学基金资助项目(60963014,60663307);江西省自然科学基金资助项目(2007GZS0186);江西省教育厅科技项目(GJJ09365);江西师范大学青年成长基金资助项目(2696)

A Collaborative Filtering Algorithm Based on Random Walkand Cluster-based Smoothing

ZHOU Jun-jun, WANG Ming-wen, HE Shi-zhu, SHI Song

College of Computer Information Engineering,Jiangxi Normal University,Nanchang Jiangxi 330022,China

Received:2010-12-22 Published:2018-11-16

摘要/Abstract

摘要： 协同过滤是电子商务推荐系统中被广泛采用的技术,然而数据稀疏性会影响协同过滤的推荐质量。本文针对数据稀疏问题提出一种基于随机游走和聚类平滑的两阶段协同过滤推荐算法。离线阶段:计算项目间相关性,提出了一个新的方法即通过加权累加各步转移概率对项目间相关性进行描述。根据得到的项目相关性矩阵对项目聚类,利用聚类信息对未评分数据进行平滑处理。在线阶段:根据离线阶段得到的项目间相关性查找目标项目的邻居并进行预测。本文提出的方法能加强项目间相关性的描述。实验表明,根据用该方法得到的项目相关性矩阵查找邻居更加准确,可以有效地缓解稀疏数据的影响,改善推荐的性能。

关键词: 协同过滤, 随机游走, 相关性描述, 聚类平滑, MAE

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 betweenitems,suggest anew method which describes the correlation between items by cumulating weightedtransition probability of each step.Cluster items according to the item correlation matrix,then smooth the unrated data by using clustering information.For on-linestage,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 results illustrate that searching neighbors according to the item correlation matrix will become more accurate,which can effectively relieve theimpact of sparse data and improve the quality of recommendation.

Key words: collaborative filtering, random walk, correlation description, cluster-based smoothing, MAE

中图分类号:

TP391

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.

ZHOU Jun-jun, WANG Ming-wen, HE Shi-zhu, SHI Song. A Collaborative Filtering Algorithm Based on Random Walkand Cluster-based Smoothing[J]. Journal of Guangxi Normal University(Natural Science Edition), 2011, 29(1): 173-178.

参考文献

[1] 罗文兵,吴润秀,王明文,等.基于结果聚类分析的个性化推荐模型[J].广西师范大学学报:自然科学版,2010,28(1):113-116.
[2] GOLDBERG K,ROEDER T,GUPTA D,et al.Eigentaste:a constant time collaborative filtering algorithm[J].Information Retrieval,2001,4(2):133-151.
[3] HOFMANN T.Latent semantic models for collaborative filtering[J].ACM Transactions on Information Systems,2004,22(1):89-115.
[4] XUE Gui-rong,LIN Chen-xi,YANG Qiang.Scalable collaborative filtering using cluster-based smoothing[C]//Proc of the 28th Annual InternationalACM SIGIR Conference on Research and Development in Information Retrieval.Brazil:ACM,2005:114-121.
[5] WANG Jun,de VRIES A P,REINDERS M J.Unifying user-based and item-based collaborative filtering approaches by similarity fusion[C]//Proc of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:ACM,2006:501-508.
[6] SOBOROFF I M,NICHOLAS C.Collaborative filtering and the generalized vector space model[C]//Proc of the 23rd Annual International Conference on Research and Development in Information Retrieval.New York:ACM,2000:351-353.
[7] SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]//Proc of the 10th International Conference on World Wide Web.New York:ACM,2001:285-295.
[8] HERLOCKER J L,KONSTAN J A,TERVEEN L G,et al.Evaluating collaborative filtering recommender systems[J].ACM Transactions on Information Systems,2004,22(1):5-53.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed