%0 Journal Article
%T A recommendation algorithm based on support vector regression<br>一种基于支持向量机回归的推荐算法
%A WANG Hong-Yu
%A MI Zhong-Chun
%A LIANG Xiao-Yan
%A YE Yue-Xiang
%A <br>王宏宇
%A 糜仲春
%A 梁晓艳
%A 叶跃祥
%J 中国科学院研究生院学报
%D 2007
%I 
%X Recommender systems and recommendation algorithm has become one of the hotspots of data mining research, with the rapid boosting of e-commerce. Support Vector Regression (SVR) algorithm has been introduced to construct a content-based recommend approach. First, the contents of rated items are analyzed with SVR to build regression model of user profiles for active users. Then use the user profiles to give recommendations. Experimental results on the EachMovie dataset show that the proposed approach has better recommend performance and less time spending than the conventional collaborative filtering approach.
%K recommender systems
%K Support Vector Regression
%K content-based recommendation<br>推荐系统
%K 支持向量机回归
%K 基于内容的推荐
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=B5EDD921F3D863E289B22F36E70174A7007B5F5E43D63598017D41BB67247657&cid=B47B31F6349F979B&jid=67CDFDECD959936E166E0F72DE972847&aid=EAD23F49C24980B35BC01FC257084095&yid=A732AF04DDA03BB3&vid=B91E8C6D6FE990DB&iid=B31275AF3241DB2D&sid=81D76BB45305F8B7&eid=0B9C8D3D9D6254B2&journal_id=1002-1175&journal_name=中国科学院研究生院学报&referenced_num=0&reference_num=16