基于改进URP模型和K近邻的推荐研究  

Recommendation Research Based on Improved URP Model and K Nearest Neighbors

作　　者：夏利民[1] 赵业东[1] 彭东亮[1] 张伟[1] 

机构地区：[1]中南大学信息科学与工程学院长沙410075

出　　处：《计算机科学》2013年第6期276-278,299,共4页Computer Science

基　　金：国家自然科学基金(50808025);国家教育部博士点基金(20090162110057)资助

摘　　要：针对传统推荐系统中存在的冷开始和准确性等问题,提出了一种基于改进URP模型和K近邻的推荐方法。该方法利用改进的URP模型对用户和项目进行建模,可以有效地解决新用户的问题;并通过推荐项目的 K近邻对预测等级进行优化,可以显著提高对新项目预测的准确性。实验结果表明,该方法可以有效地解决冷开始问题,并显著提高推荐结果的准确性。The methods used to recommend products suffer from the problems such as cold starting and accurate. To address these problems, a new recommendation method based on improved URP model and K nearest neighbors was proposed. Users and items are modeled by improved URP model, and this model can solve the new user problem effec- tively. The rates predicted are optimized by K nearest neighbors to solve the new item problem. The experimental re- sults show that the new method has good quality for recommendation.

关 键 词：URP模型 K近邻 产生过程 GIBBS抽样 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]