Addressing Confounding Feature Issue for Causal Recommendation

TL;DR

This paper introduces a causal inference framework called Deconfounding Causal Recommendation (DCR) to mitigate confounding feature bias in recommender systems, improving prediction accuracy without significant additional computational cost.

Contribution

The work formulates confounding feature issues from a causal perspective and proposes DCR with a mixture-of-experts architecture to efficiently remove bias in recommendations.

Findings

DCR improves recommendation accuracy over baseline models.

The mixture-of-experts architecture reduces inference time.

DCR effectively addresses confounding bias in real-world datasets.

Abstract

In recommender system, some feature directly affects whether an interaction would happen, making the happened interactions not necessarily indicate user preference. For instance, short videos are objectively easier to be finished even though the user does not like the video. We term such feature as confounding feature, and video length is a confounding feature in video recommendation. If we fit a model on such interaction data, just as done by most data-driven recommender systems, the model will be biased to recommend short videos more, and deviate from user actual requirement. This work formulates and addresses the problem from the causal perspective. Assuming there are some factors affecting both the confounding feature and other item features, e.g., the video creator, we find the confounding feature opens a backdoor path behind user item matching and introduces spurious correlation. To remove the effect of backdoor path, we propose a framework named Deconfounding Causal Recommendation (DCR), which performs intervened inference with do-calculus. Nevertheless, evaluating do calculus requires to sum over the prediction on all possible values of confounding feature, significantly increasing the time cost. To address the efficiency challenge, we further propose a mixture-of experts (MoE) model architecture, modeling each value of confounding feature with a separate expert module. Through this way, we retain the model expressiveness with few additional costs. We demonstrate DCR on the backbone model of neural factorization machine (NFM), showing that DCR leads to more accurate prediction of user preference with small inference time cost.