Item-based relevance modelling of recommendations for getting rid of long tail products

作者：

Highlights：

• The liquidation of long tail items can be assisted by recommender systems.

• We propose a probabilistic item-based Relevance Model (IRM2).

• IRM2 outperforms state-of-the-art recommenders for long tail liquidation.

摘要

•The liquidation of long tail items can be assisted by recommender systems.•We propose a probabilistic item-based Relevance Model (IRM2).•IRM2 outperforms state-of-the-art recommenders for long tail liquidation.

论文关键词：Recommender systems,Collaborative filtering,Relevance models,Long tail

论文评审过程：Received 2 December 2015, Revised 25 February 2016, Accepted 22 March 2016, Available online 23 April 2016, Version of Record 5 May 2016.

论文官网地址：https://doi.org/10.1016/j.knosys.2016.03.021