作　　者： ; ; ; ;

机构地区： 中南大学信息科学与工程学院

出　　处： 《计算机科学》 2013年第6期276-278,299,共4页

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： 模型 近邻 产生过程 抽样

领　　域： [自动化与计算机技术] [自动化与计算机技术]