A clustering based approach to improving the efficiency of collaborative filtering recommendation

作者：

Highlights：

• Developing an efficient collaborative filtering recommender system for E-commerce.

• Applying a self-constructing clustering algorithm to reduce the dimensionality related to the products.

• Reducing the huge correlation graph to a much smaller graph for faster computation.

• Demonstrating that efficiency is greatly Improved without degradation of the recommendation quality.

摘要

•Developing an efficient collaborative filtering recommender system for E-commerce.•Applying a self-constructing clustering algorithm to reduce the dimensionality related to the products.•Reducing the huge correlation graph to a much smaller graph for faster computation.•Demonstrating that efficiency is greatly Improved without degradation of the recommendation quality.

论文关键词：Collaborative filtering recommender system,Correlation graph,Self-constructing clustering,Dimensionality reduction,Ranking algorithm

论文评审过程：Received 5 August 2015, Revised 27 March 2016, Accepted 5 May 2016, Available online 7 May 2016, Version of Record 26 May 2016.

论文官网地址：https://doi.org/10.1016/j.elerap.2016.05.001