A hybrid collaborative filtering recommendation mechanism for P2P networks

被引:57
|
作者
Liu, Zhaobin [1 ]
Qu, Wenyu [1 ]
Li, Haitao [2 ]
Xie, Changsheng [3 ]
机构
[1] Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian 116026, Peoples R China
[2] Chinese Acad Surveying & Mapping, Inst Photogrammetry & Remote Sensing, Beijing 100039, Peoples R China
[3] Huazhong Univ Sci & Technol, WNLO, Wuhan 430074, Peoples R China
来源
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2010年 / 26卷 / 08期
基金
中国国家自然科学基金;
关键词
Collaborative filtering; Recommendation; Sparse matrix; Eigenvalue matrix; Peer-to-peer (P2P) networks;
D O I
10.1016/j.future.2010.04.002
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
With the increasing number of commerce facilities using peer-to-peer (P2P) networks, challenges exist in recommending interesting or useful products and services to a particular customer. Collaborative Filtering (CF) is one of the most successful techniques that attempts to recommend items (such as music, movies, web sites) which are likely to be of interest to the people. However, conventional collaborative filtering encounters a number of challenges on its recommendation accuracy. One of the most important challenges may be due to the sparse attributes inherent to the rating data. Another important challenge is that existing CF methods consider mainly user-based or item-based ratings respectively. In this paper a P2P-based hybrid collaborative filtering mechanism for the support of combining user-based and item attribute-based ratings is considered. We take advantage of the inherent item attributes to construct a Boolean matrix to predict the blank elements for a sparse user-item matrix. Furthermore, a Hybrid collaborative filtering (HCF) algorithm is presented to improve the predictive accuracy. Case studies and experiment results illustrate that our approaches not only contribute to predicting the unrated blank data for a sparse matrix but also improve the prediction accuracy as expected. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:1409 / 1417
页数:9
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