A new user similarity model to improve the accuracy of collaborative filtering

作者：

Highlights：

• We first analyze the shortages of the existing similarity measures in collaborative filtering.

• And second, we propose a new user similarity model to overcome these drawbacks.

• We compare the new model with many other similarity measures on two real data sets.

• Experiments show that the new model can reach better performance than many existing similarity measures.

摘要

•We first analyze the shortages of the existing similarity measures in collaborative filtering.•And second, we propose a new user similarity model to overcome these drawbacks.•We compare the new model with many other similarity measures on two real data sets.•Experiments show that the new model can reach better performance than many existing similarity measures.

论文关键词：Collaborative filtering,User similarity,Recommended precision,Cold user,Recommender systems

论文评审过程：Received 19 February 2013, Revised 3 November 2013, Accepted 7 November 2013, Available online 20 November 2013.

论文官网地址：https://doi.org/10.1016/j.knosys.2013.11.006