Mitigating Filter Bubble from the Perspective of Community Detection: A Universal Framework

TL;DR

This paper introduces CD-CGCN, a universal, model-agnostic framework that uses community detection and adversarial learning to mitigate filter bubbles in recommender systems, improving diversity without sacrificing accuracy.

Contribution

It proposes a novel community detection-based framework that can be integrated into existing recommenders to reduce filter bubbles through community reweighting and adversarial training.

Findings

Effective in reducing filter bubble effects in real-world datasets.

Maintains recommendation accuracy while increasing diversity.

Outperforms baseline models in capturing inter-community preferences.

Abstract

In recent years, recommender systems have primarily focused on improving accuracy at the expense of diversity, which exacerbates the well-known filter bubble effect. This paper proposes a universal framework called CD-CGCN to address the filter bubble issue in recommender systems from a community detection perspective. By analyzing user-item interaction histories with a community detection algorithm, we reveal that state-of-the-art recommendations often focus on intra-community items, worsening the filter bubble effect. CD-CGCN, a model-agnostic framework, integrates a Conditional Discriminator and a Community-reweighted Graph Convolutional Network which can be plugged into most recommender models. Using adversarial learning based on community labels, it counteracts the extracted community attributes and incorporates an inference strategy tailored to the user's specific filter bubble state. Extensive experiments on real-world datasets with multiple base models validate its effectiveness in mitigating filter bubbles while preserving recommendation quality. Additionally, by applying community debiasing to the original test set to construct an unbiased test set, we observe that CD-CGCN demonstrates superior performance in capturing users' inter-community preferences.