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Mitigating Hidden Confounding Effects for Causal Recommendation | IEEE Journals & Magazine | IEEE Xplore

Mitigating Hidden Confounding Effects for Causal Recommendation


Abstract:

Recommender systems suffer from confounding biases when there exist confounders affecting both item features and user feedback (e.g., like or not). Existing causal recomm...Show More

Abstract:

Recommender systems suffer from confounding biases when there exist confounders affecting both item features and user feedback (e.g., like or not). Existing causal recommendation methods typically assume confounders are fully observed and measured, forgoing the possible existence of hidden confounders in real applications. For instance, product quality is a confounder since it affects both item prices and user ratings, but is hidden for the third-party e-commerce platform due to the difficulty of large-scale quality inspection; ignoring it could result in the bias effect of over-recommending high-price items. This work analyzes and addresses the problem from a causal perspective. The key lies in modeling the causal effect of item features on a user's feedback. To mitigate hidden confounding effects, it is compulsory but challenging to estimate the causal effect without measuring the confounder. Towards this goal, we propose a Hidden Confounder Removal (HCR) framework that leverages front-door adjustment to decompose the causal effect into two partial effects, according to the mediators between item features and user feedback. The partial effects are independent from the hidden confounder and identifiable. During training, HCR performs multi-task learning to infer the partial effects from historical interactions. We instantiate HCR for two scenarios and conduct experiments on three real-world datasets. Empirical results show that the HCR framework provides more accurate recommendations, especially for less-active users. We will release the code once accepted.
Published in: IEEE Transactions on Knowledge and Data Engineering ( Volume: 36, Issue: 9, September 2024)
Page(s): 4794 - 4805
Date of Publication: 19 March 2024

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I. Introduction

Data-driven models have become the default choice for building personalized recommendation services [1], [2]. These models typically focus on the correlation between item attributes and user feedback, suffering from the confounding bias [3], [4]. The source of such bias is the confounder that affects item attributes and user feedback simultaneously, leading to spurious correlations [5], [6]. For instance, the high quality of an item is the driving factor behind its high price, and it also tends to generate more positive ratings from users, resulting in a spurious correlation between high price and high rating. Fitting the data solely based on this correlation can lead to the over-recommendation of high-price items. Worse still, the confounding effect will hurt the fairness across item producers and make the model vulnerable to be attacked, e.g., some producers may intentionally increase the price for more exposure opportunities. It is thus essential to mitigate the confounding effect in recommendation.

References

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