%0 Journal Article
%T A Collaborative Filtering Recommendation Algorithm Based on Influence Sets<br>基于影响集的协作过滤推荐算法
%A CHEN Jian
%A YIN Jian
%A <br>陈健
%A 印鉴
%J 软件学报
%D 2007
%I 
%X The traditional user-based collaborative filtering (CF) algorithms often suffer from two important problems: Scalability and sparsity because of its memory-based k nearest neighbor query algorithm. Item-Based CF algorithms have been designed to deal with the scalability problems associated with user-based CF approaches without sacrificing recommendation or prediction accuracy. However, item-based CF algorithms still suffer from the data sparsity problems. This paper presents a CF recommendation algorithm, named CFBIS (collaborative filtering based on influence sets), which is based on the concept of influence set and is a hot topic in information retrieval system. Moreover, it defines a new prediction computation method for this new recommendation mechanism. Experimental results show that the algorithm can achieve better prediction accuracy than traditional item-based CF algorithms. Furthermore, the algorithm can alleviate the dataset sparsity problem.
%K E-commerce
%K recommendation system
%K collaborative filtering
%K influence set<br>电子商务
%K 推荐系统
%K 协作过滤
%K 影响集
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=7CCF8DAB4989A41D&yid=A732AF04DDA03BB3&vid=13553B2D12F347E8&iid=DF92D298D3FF1E6E&sid=380BA99738E9DB23&eid=B9018D9DCA7DD012&journal_id=1000-9825&journal_name=软件学报&referenced_num=11&reference_num=21