Debiased Recommendation with User Feature Balancing

TL;DR

This paper introduces a novel debiased recommendation framework that uses user feature balancing to achieve unbiased learning without relying on inverse propensity scores, improving recommendation accuracy.

Contribution

The paper proposes the first debiased recommendation method based on confounder balancing and introduces strategies for user distribution balancing to enhance offline recommendation models.

Findings

Effective in promoting recommendation performance across datasets

Outperforms state-of-the-art methods in experiments

Reduces bias without inverse propensity score estimation

Abstract

Debiased recommendation has recently attracted increasing attention from both industry and academic communities. Traditional models mostly rely on the inverse propensity score (IPS), which can be hard to estimate and may suffer from the high variance issue. To alleviate these problems, in this paper, we propose a novel debiased recommendation framework based on user feature balancing. The general idea is to introduce a projection function to adjust user feature distributions, such that the ideal unbiased learning objective can be upper bounded by a solvable objective purely based on the offline dataset. In the upper bound, the projected user distributions are expected to be equal given different items. From the causal inference perspective, this requirement aims to remove the causal relation from the user to the item, which enables us to achieve unbiased recommendation, bypassing the computation of IPS. In order to efficiently balance the user distributions upon each item pair, we propose three strategies, including clipping, sampling and adversarial learning to improve the training process. For more robust optimization, we deploy an explicit model to capture the potential latent confounders in recommendation systems. To the best of our knowledge, this paper is the first work on debiased recommendation based on confounder balancing. In the experiments, we compare our framework with many state-of-the-art methods based on synthetic, semi-synthetic and real-world datasets. Extensive experiments demonstrate that our model is effective in promoting the recommendation performance.