Improving accuracy and diversity of personalized recommendation through power law adjustments of user similarities
作者：
Highlights：
• The existence of popular objects may yield unreasonable recommendation results.
• We propose a method called PLUS to adjust user similarities using a power function.
• We show the superior performance of PLUS by large-scale validation experiments.
• PLUS achieves a reasonable tradeoff between recommendation accuracy and diversity.
• PLUS is robust to similarity measures and consistent between different data sets.
摘要
•The existence of popular objects may yield unreasonable recommendation results.•We propose a method called PLUS to adjust user similarities using a power function.•We show the superior performance of PLUS by large-scale validation experiments.•PLUS achieves a reasonable tradeoff between recommendation accuracy and diversity.•PLUS is robust to similarity measures and consistent between different data sets.
论文关键词：Recommender systems,Collaborative filtering,Power law adjustment,Accuracy,Diversity
论文评审过程：Received 27 May 2012, Revised 26 January 2013, Accepted 28 March 2013, Available online 6 April 2013.
论文官网地址：https://doi.org/10.1016/j.dss.2013.03.006
