Research of collaborative filtering recommender method using WUM and RBFN to fill missing values

Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (9): 22-26.

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Research of collaborative filtering recommender method using WUM and RBFN to fill missing values

XUE Fuliang1，2, ZHANG Huiying1

1.College of Management & Economics, Tianjin University, Tianjin 300072, China
2.Department of Information Management System, Tianjin University of Finance & Economics, Tianjin 300222, China

Received:1900-01-01 Revised:1900-01-01 Online:2012-03-21 Published:2012-04-11

应用WUM和RBFN补值的协同过滤推荐研究

薛福亮1，2，张慧颖1

1.天津大学管理与经济学部，天津 300072
2.天津财经大学商学院管理信息系统系，天津 300222

Abstract

Abstract: Collaborative filtering is the most successful method in recommender system, but it has cold start, sparsity, scalability and other issues when faced with sparse data. Web Usage Mining（WUM） is proposed to obtain implicit data which can fill explicit user rating matrix, and Radial Basis Function Network（RBFN） is used to smoothen filled rating matrix to get a complete rating matrix. Collaborative filtering is used to classify similar users based on smoothed complete rating matrix and generate recommendation. Experimental results show that compared with traditional collaborative filtering methods, the proposed method is more effective both in the accuracy or relevance of recommendations.

Key words: recommender system, collaborative filtering, Web Usage Mining, radial basis function network

摘要： 协同过滤是目前推荐系统中最为成功的一种方法，但面临稀疏数据特征时存在冷启动、稀疏性、可扩展性等问题。提出利用Web数据挖掘（WUM）获取隐性数据对显性用户评价矩阵进行补值，应用径向基函数（RBFN）对补值后的评价矩阵进一步进行平滑处理，得到消除稀疏性后的完全评价矩阵，基于完全评价矩阵利用协同过滤技术对相似用户进行聚类并实施推荐。实验评价结果表明该方法与传统协同过滤推荐方法相比，无论在推荐精度还是推荐相关性上都更为有效。

关键词: 推荐系统, 协同过滤, 网络数据挖掘, 径向基函数

XUE Fuliang1，2, ZHANG Huiying1. Research of collaborative filtering recommender method using WUM and RBFN to fill missing values[J]. Computer Engineering and Applications, 2012, 48(9): 22-26.

薛福亮1，2，张慧颖1. 应用WUM和RBFN补值的协同过滤推荐研究[J]. 计算机工程与应用, 2012, 48(9): 22-26.

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Research of collaborative filtering recommender method using WUM and RBFN to fill missing values

应用WUM和RBFN补值的协同过滤推荐研究

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