Escaping your comfort zone: A graph-based recommender system for finding novel recommendations among relevant items

作者：

Highlights：

• A recommender system based on a positively-related item-graph targeted for novel and relevant recommendations is proposed.

• A live test was performed comparing the proposed system with a state-of-the-art matrix factorization algorithm.

• The proposed system consistently outperforms matrix factorization in finding items that are both novel and relevant.

• By finding novel and relevant items, the system addresses popularity bias commonly found in collaborative filtering-based recommender systems.

摘要

•A recommender system based on a positively-related item-graph targeted for novel and relevant recommendations is proposed.•A live test was performed comparing the proposed system with a state-of-the-art matrix factorization algorithm.•The proposed system consistently outperforms matrix factorization in finding items that are both novel and relevant.•By finding novel and relevant items, the system addresses popularity bias commonly found in collaborative filtering-based recommender systems.

论文关键词：Recommender systems,Novelty,Relevance,Popularity bias,Music recommendation

论文评审过程：Available online 12 August 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.07.024