Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering

TL;DR

This paper investigates the impact of graph-based collaborative filtering on popularity bias and proposes a normalization method to balance accuracy and novelty in recommender systems.

Contribution

It provides a theoretical analysis of popularity bias in graph-based CF and introduces r-AdjNorm, a simple plugin to improve the accuracy-novelty trade-off.

Findings

Symmetric neighborhood aggregation worsens popularity bias as depth increases.

r-AdjNorm effectively balances accuracy and novelty.

The method improves novelty without losing accuracy across datasets.

Abstract

Recent years have witnessed the great accuracy performance of graph-based Collaborative Filtering (CF) models for recommender systems. By taking the user-item interaction behavior as a graph, these graph-based CF models borrow the success of Graph Neural Networks (GNN), and iteratively perform neighborhood aggregation to propagate the collaborative signals. While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate popularity bias of recommender systems?" To answer this question, we first investigate the two-fold performances w.r.t. accuracy and novelty for existing graph-based CF methods. The empirical results show that symmetric neighborhood aggregation adopted by most existing graph-based CF models exacerbate the popularity bias and this phenomenon becomes more serious as the depth of graph propagation increases. Further, we theoretically analyze the cause of popularity bias for graph-based CF. Then, we propose a simple yet effective plugin, namely r-AdjNorm, to achieve an accuracy-novelty trade-off by controlling the normalization strength in the neighborhood aggregation process. Meanwhile, r-AdjNorm can be smoothly applied to the existing graph-based CF backbones without additional computation. Finally, experimental results on three benchmark datasets show that our proposed method can improve novelty without sacrificing accuracy under various graph-based CF backbones.