CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation

TL;DR

CausalRec introduces a causal inference framework to identify and eliminate visual bias in visually-aware recommendation systems, improving prediction accuracy by focusing on real user preferences.

Contribution

The paper proposes a model-agnostic causal inference approach, CausalRec, to effectively remove visual bias and enhance recommendation quality in visually-aware systems.

Findings

CausalRec achieves state-of-the-art performance on eight benchmark datasets.

The causal framework successfully reduces visual bias in recommendations.

Extensive experiments validate the effectiveness of the debiasing method.

Abstract

Visually-aware recommendation on E-commerce platforms aims to leverage visual information of items to predict a user's preference. It is commonly observed that user's attention to visual features does not always reflect the real preference. Although a user may click and view an item in light of a visual satisfaction of their expectations, a real purchase does not always occur due to the unsatisfaction of other essential features (e.g., brand, material, price). We refer to the reason for such a visually related interaction deviating from the real preference as a visual bias. Existing visually-aware models make use of the visual features as a separate collaborative signal similarly to other features to directly predict the user's preference without considering a potential bias, which gives rise to a visually biased recommendation. In this paper, we derive a causal graph to identify and analyze the visual bias of these existing methods. In this causal graph, the visual feature of an item acts as a mediator, which could introduce a spurious relationship between the user and the item. To eliminate this spurious relationship that misleads the prediction of the user's real preference, an intervention and a counterfactual inference are developed over the mediator. Particularly, the Total Indirect Effect is applied for a debiased prediction during the testing phase of the model. This causal inference framework is model agnostic such that it can be integrated into the existing methods. Furthermore, we propose a debiased visually-aware recommender system, denoted as CausalRec to effectively retain the supportive significance of the visual information and remove the visual bias. Extensive experiments are conducted on eight benchmark datasets, which shows the state-of-the-art performance of CausalRec and the efficacy of debiasing.