A hybrid collaborative filtering recommendation mechanism for P2P networks

Authors:
Zhaobin Liu;Wenyu Qu;Haitao Li;Changsheng Xie
Affiliations:
School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, PR China;School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, PR China;Institute for Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing, 100039, PR China;Wuhan National Lab for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, 430074, PR China
Venue:
Future Generation Computer Systems
Year:
2010

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Abstract

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.