基于P2P网络的协同过滤推荐算法的研究与实现

协同过滤算法是当前电子商务推荐系统最有效的信息过滤技术之一。而传统协同过滤算法的最大弱点是可扩展性问题，随着用户数量以及商品项目的增加，计算复杂度的快速增长导致大规模电子商务系统的可扩展性问题．本文提出了一种基于P2P网络协同过滤推荐算法方法，采用对等计算的方法进行用户数据库的管理和评分预测工作，该系统充分利用P2P网络对等计算的优点，采用了多生成树的路由算法。实验数据表明了我们采用的基于P2P网络的分布式协同过滤方法较传统集中式算法有更好的可扩展性和预测准确性．

Research and Implementation of Collaborative Filtering Recommendation Algorithm Based on P2P Network

Collaborative filtering technique is the one of most effective information filtering techniques in E-Commerce recommending systems presently. Traditional collaborative filtering algorithms are suffered from its shortage in scalability as their calculation complexity increased quickly both in time and space when the records in user database increases, which has affect the development of large-scale E-Commerce system. This article proposed a new distributed algorithm, namely collaborative filtering recommendation algorithm based on P2P network, which made use of the P2P application advantages sufficiently and adopt multi-spanning tree routing algorithm, in order to manage user database and predictive rating work. The experiment result showed that the new collaborative filtering based P2P network algorithm had much better scalability and predictived rating accuracy than traditional centralized ones.