Topology-Aware Popularity Debiasing via Simplicial Complexes

• URL: http://arxiv.org/abs/2411.13892v1
• Date: Thu, 21 Nov 2024 07:12:47 GMT
• Title: Topology-Aware Popularity Debiasing via Simplicial Complexes
• Authors: Yanbiao Ji, Yue Ding, Chang Liu, Yuxiang Lu, Xin Xin, Hongtao Lu,
• Abstract summary: Test-time Simplicial Propagation (TSP) incorporates simplicial complexes (SCs) to enhance the expressiveness of Graph Neural Networks (GNNs) Our approach captures multi-order relationships through SCs, providing a more comprehensive representation of user-item interactions. Our method produces more uniform distributions of item representations, leading to fairer and more accurate recommendations.
• Score: 19.378410889819165
• License:
• 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.

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