基于非结构化P2P网络用户模型的协同过滤推荐机制

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

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。

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.