A hybrid collaborative filtering recommendation mechanism for P2P networks
A hybrid collaborative filtering recommendation mechanism for P2P networks
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DOI：
10.1016/j.future.2010.04.002
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发表时间：
2010-10
期刊：
影响因子：
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通讯作者：
Zhaobin Liu;W. Qu;Haitao Li;C. Xie
中科院分区：
文献类型：
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作者：
Zhaobin Liu;W. Qu;Haitao Li;C. Xie
随着使用点对点(P2P)网络的商业设施的数量不断增加，向特定客户推荐有趣或有用的产品和服务存在挑战。协作过滤(CF)是最成功的技术之一，它试图推荐人们可能感兴趣的项目(如音乐、电影、网站)。然而，传统的协同过滤在其推荐准确性方面遇到了许多挑战。最重要的挑战之一可能是由于评级数据固有的稀疏属性。另一个重要的挑战是，现有的CF方法主要分别考虑基于用户或基于项目的评级。本文提出了一种基于P2P的混合式协同过滤机制，支持基于用户和基于项目属性的评分相结合。对于稀疏的用户-项目矩阵，我们利用其固有的项目属性构造一个布尔矩阵来预测空白元素。此外，为了提高预测精度，提出了一种混合协同过滤(HCF)算法。实例研究和实验结果表明，该方法不仅有助于对稀疏矩阵中未评级的空白数据进行预测，而且预测精度也达到了预期的水平。
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.