%0 Journal Article
%T A Collaborative Filtering Recommendation Algorithm Based on Item Rating Prediction<br>基于项目评分预测的协同过滤推荐算法
%A DENG Ai-Lin
%A ZHU Yang-Yong
%A SHI Bai-Le
%A <br>邓爱林
%A 朱扬勇
%A 施伯乐
%J 软件学报
%D 2003
%I 
%X Recommendation system is one of the most important technologies in E-commerce. With the development of E-commerce, the magnitudes of users and commodities grow rapidly, resulted in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To address this issue a novel collaborative filtering algorithm based on item rating prediction is proposed. This method predicts item ratings that users have not rated by the similarity of items, then uses a new similarity measure to find the target users?neighbors. The experimental results show that this method can efficiently improve the extreme sparsity of user rating data, and provid better recommendation results than traditional collaborative filtering algorithms.
%K E-commerce
%K recommendation system
%K collaborative filtering
%K item similarity
%K recommendation algorithm
%K MAE (mean absolute error)<br>电子商务
%K 推荐系统
%K 协同过滤
%K 项目相似性
%K 推荐算法
%K 平均绝对偏差
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=8C991A4B851077D4&yid=D43C4A19B2EE3C0A&vid=F3583C8E78166B9E&iid=9CF7A0430CBB2DFD&sid=15347EAE303D7206&eid=99C22CF1E519BF36&journal_id=1000-9825&journal_name=软件学报&referenced_num=117&reference_num=13