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

by   Ruihong Qiu, et al.

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.


Causal Inference for Knowledge Graph based Recommendation

Knowledge Graph (KG), as a side-information, tends to be utilized to sup...

Causal Intervention for Leveraging Popularity Bias in Recommendation

Recommender system usually faces popularity bias issues: from the data p...

Causal Inference for Chatting Handoff

Aiming to ensure chatbot quality by predicting chatbot failure and enabl...

Price-aware Recommendation with Graph Convolutional Networks

In recent years, much research effort on recommendation has been devoted...

Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders

Recent studies on Next-basket Recommendation (NBR) have achieved much pr...

"Click" Is Not Equal to "Like": Counterfactual Recommendation for Mitigating Clickbait Issue

Recommendation is a prevalent and critical service in information system...

A Neural Network Model of Lexical Competition during Infant Spoken Word Recognition

Visual world studies show that upon hearing a word in a target-absent vi...

Please sign up or login with your details

Forgot password? Click here to reset