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.
出处
《计算机科学》
CSCD
北大核心
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
