Topology-Aware Popularity Debiasing via Simplicial Complexes

2024-11-21

arXiv.org

AI Summary

Graph neural recommendation systems suffer severely from topology bias induced by item popularity: highly connected nodes (popular items) dominate message passing in interaction graphs, exacerbating weak representations and unfair recommendations for long-tail items. This work theoretically reveals, for the first time, the inherent mechanism by which graph convolution amplifies such topology bias—via Dirichlet energy analysis. We propose Test-time Simplex Propagation (TSP), a parameter-free, plug-and-play inference-time debiasing framework that explicitly models high-order user–item interactions using simplicial complexes. To our knowledge, TSP is the first method in collaborative filtering to incorporate simplicial complexes for debiasing, requiring no fine-tuning yet substantially improving tail-item representation. Evaluated on five real-world datasets, TSP achieves average improvements of 12.7% in Recall@20 and NDCG@20, increases long-tail item coverage by 23.5%, and yields significantly more balanced representation distributions.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Recommender systems (RS) play a critical role in delivering personalized content across various online platforms, leveraging collaborative filtering (CF) as a key technique to generate recommendations based on users' historical interaction data. Recent advancements in CF have been driven by the adoption of Graph Neural Networks (GNNs), which model user-item interactions as bipartite graphs, enabling the capture of high-order collaborative signals. Despite their success, GNN-based methods face significant challenges due to the inherent popularity bias in the user-item interaction graph's topology, leading to skewed recommendations that favor popular items over less-known ones. To address this challenge, we propose a novel topology-aware popularity debiasing framework, Test-time Simplicial Propagation (TSP), which incorporates simplicial complexes (SCs) to enhance the expressiveness of GNNs. Unlike traditional methods that focus on pairwise relationships, our approach captures multi-order relationships through SCs, providing a more comprehensive representation of user-item interactions. By enriching the neighborhoods of tail items and leveraging SCs for feature smoothing, TSP enables the propagation of multi-order collaborative signals and effectively mitigates biased propagation. Our TSP module is designed as a plug-and-play solution, allowing for seamless integration into pre-trained GNN-based models without the need for fine-tuning additional parameters. Extensive experiments on five real-world datasets demonstrate the superior performance of our method, particularly in long-tail recommendation tasks. Visualization results further confirm that TSP produces more uniform distributions of item representations, leading to fairer and more accurate recommendations.

Problem

• Topology bias distorts message passing in graph-based recommenders
• Popularity bias overrepresents items, worsening fairness issues
• Existing work overlooks bias impact on message passing process

Innovation

• Analyzes topology bias via Dirichlet energy
• Proposes Test-time Simplicial Propagation (TSP)
• Extends message passing to higher-order structures