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J4 ›› 2011, Vol. 46 ›› Issue (5): 28-33.

• SEWM 2011 会议 • 上一篇    下一篇

基于非结构化P2P网络用户模型的协同过滤推荐机制

刘健1,尹春霞2*,原福永3   

  1. 1.河北外国语职业学院基础部, 河北 秦皇岛 066311; 2.河北农业大学海洋学院, 河北 秦皇岛 066003;
    3.燕山大学信息科学与工程学院, 河北 秦皇岛 066004
  • 收稿日期:2010-12-06 发布日期:2011-05-25
  • 通讯作者: 尹春霞(1982- ),女,讲师,硕士,研究方向为信息检索.Email:lovelyfatbear@163.com
  • 作者简介:刘健(1981- ),男,助教,硕士,研究方向为P2P计算.Email:chinafather@163.com
  • 基金资助:

    河北省科技支撑计划项目 (072135208);秦皇岛市科学技术研究与发展计划项目 (200901A041)

A collaborative filtering recommendation mechanism based on user profile in unstructured P2P networks

LIU Jian1, YIN Chun-xia 2*, YUAN Fu-yong3   

  1. 1. Basic Department, Hebei Vocational College of Foreign Languages, Qinhuangdao 066311, Hebei,  China;
    2. College of Ocean, Hebei Agricultural University, Qinhuangdao 066003, Hebei, China;
    3. College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China
  • Received:2010-12-06 Published:2011-05-25

摘要:

协同过滤是当前应用在信息推荐系统中最成功的技术之一。但随着用户数量和所需过滤信息的增加,计算复杂度迅速增长,大多数推荐系统都因集中式的体系结构而面临可扩展性差的问题。本文提出了一种基于非结构化P2P网络的协同过滤推荐机制,采用基于词汇链的方法构建资源对象描述向量,建立由偏好资源对象集合构成的用户模型,并且根据用户的兴趣变化,通过动态邻居重组的方法获得实时的个性化推荐。实验数据表明采用基于非结构化P2P网络的协同过滤推荐机制较传统集中式推荐方案有更好的可扩展性和预测准确性。

关键词: P2P;协同过滤;用户模型;个性化推荐

Abstract:

Nowadays, collaborative filtering is one of the most successful technologies applyed in information recommender systems. However, with increase of the number of users and the amount of information needed to filter, the systems′ computational complexity quickly increases, and most centralized recommender systems have to face the low scalability problem. To solve the scalability problem of the recommender systems, a distributed collaborative filtering recommendation mechanism with an unstructured P2P architecture is proposed. In the recommendation mechanism, the content of resource is  represented by a vector according to the lexical chain method, and then the user profile can be represented by a preferred resource set. In addition, with the change of the user′s interest, the proposed mechanism also utilizes dynamic neighbor peer set reformation to gain a real time personalized recommendation.

Key words:  P2P; collaborative filtering; user profile; personalized recommendation

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