General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout

An Zhang, Wenchang Ma, Pengbo Wei, Leheng Sheng, Xiang Wang

Keywords: Collaborative filtering, Debiasing, General Debias

Abstract:

Graph neural networks (GNNs) have shown impressive performance in recommender systems, particularly in collaborative filtering (CF). The key lies in aggregating neighborhood information on a user-item interaction graph to enhance user/item representations. However, we have discovered that this aggregation mechanism comes with a drawback – it amplifies biases present in the interaction graph.
For instance, a user's interactions with items can be driven by both unbiased true interest and various biased factors like item popularity or exposure. But the current aggregation approach combines all information, both biased and unbiased, leading to biased representation learning. Consequently, graph-based recommenders can learn distorted views of users/items, hindering the modeling of their true preferences and generalization.

To address this issue, we introduce a novel framework called \underline{Adv}ersarial Graph \underline{Drop}out (AdvDrop). It differentiates between unbiased and biased interactions, enabling unbiased representation learning. For each user/item, AdvDrop employs adversarial learning to split the neighborhood into two views: one with bias-mitigated interactions and the other with bias-augmented interactions. After view-specific aggregation, AdvDrop ensures that the bias-mitigated and bias-augmented representations remain invariant, shielding them from the influence of bias.
We validate AdvDrop's effectiveness on six public datasets that cover both general and specific biases, demonstrating significant improvements. Furthermore, our method exhibits meaningful separation of subgraphs and achieves unbiased representations for graph-based CF models, as revealed by in-depth analysis.

Track: User Modeling and Recommendation

Submission Guidelines Scope: Yes

Submission Guidelines Blind: Yes

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Student Author: No

Submission Number: 2121