  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.
