Accuracy-diversity trade-off in recommender systems via graph convolutions

作者:

Highlights:

• We propose a novel accuracy-diversity trade-off for recommender systems with graph convolutional filters and neural networks.

• The approach leverages joint information from the nearest and furthest neighbours to balance accuracy with diversity.

• We develop a parameter design framework applicable both in a learning-to-rate and learning to-rank setting.

• We provide a graph special analysis of the proposed method to explain how ratings are exploited for the trade-off.

• Experimental evaluation demonstrates the proposed approach outperforms competing alternatives for establishing an accuracy-diversity trade-off.

摘要

•We propose a novel accuracy-diversity trade-off for recommender systems with graph convolutional filters and neural networks.•The approach leverages joint information from the nearest and furthest neighbours to balance accuracy with diversity.•We develop a parameter design framework applicable both in a learning-to-rate and learning to-rank setting.•We provide a graph special analysis of the proposed method to explain how ratings are exploited for the trade-off.•Experimental evaluation demonstrates the proposed approach outperforms competing alternatives for establishing an accuracy-diversity trade-off.

论文关键词:Accuracy-diversity,Collaborative filtering,Graph filters,Graph convolutional neural networks,Graph signal processing

论文评审过程:Received 29 July 2020, Revised 7 November 2020, Accepted 3 December 2020, Available online 14 December 2020, Version of Record 14 December 2020.

论文官网地址:https://doi.org/10.1016/j.ipm.2020.102459