Academic Journals Database
Disseminating quality controlled scientific knowledge

优化的协同过滤推荐算法 Optimized Cooperative Filtering Algorithm

Author(s): 马瑞新 | 孟繁成 | 王涵杨

Journal: Computer Science and Application
ISSN 2161-8801

Volume: 01;
Issue: 03;
Start page: 108;
Date: 2011;
Original page

Keywords: 社会网络 | 信任度 | 个性化推荐 | 最近邻居 | Social Network | Trust | Personalized Recommendation | The Nearest Neighbor

基于最近邻的协同过滤算法是个性化推荐中应用的最为成功的算法,然而在普通网络中,用户身份兴趣等信息缺乏可靠性,导致该算法推荐不够精确。社会网络是彼此拥有足够的感情后建立起来的一种人际关系网络,它的出现为广大用户提供了一个相对安全的绿色交流通道。本文针对社会网络中个性化推荐系统存在的问题,提出了一种在社会网络下基于信任度的最近邻居集合算法的优化,该算法通过分析社会网络中用户的信任度给出个性化推荐。实验表明,该算法在社会网络中的应用提高了个性化推荐的准确度,增加了用户的满意程度。Cooperative Filtering algorithm is one of the most successful technologies in the application of personalized recommendation system. However, the users’ identities and interests are very complicated in the normal network, which results in the inaccuracy of the recommendation. However, the appearance of social network provides users with a relatively green and safe communication platform. This paper in terms of the problems in the personalized recommendation system of social network comes up with the trust-based nearest neighbor algorithm which optimizes the traditional NN algorithm and provides recommendation by analyzing trust between the social network users. The experimental results show that the modified algorithm greatly improves the recommendation accuracy and raises the users’ degree of satisfaction.

Tango Rapperswil
Tango Rapperswil

     Affiliate Program