Propensity Matters: Measuring and Enhancing Balancing for Recommendation

EasyChair Preprint 13095

Abstract

Propensity-based weighting methods have been widely studied and demonstrated competitive performance in debiased recommendations. Nevertheless, there are still many questions to be addressed. How to estimate the propensity more conducive to debiasing performance? Which metric is more reasonable to measure the quality of the learned propensities? Is it better to make the cross-entropy loss as small as possible when learning propensities? In this paper, we first discuss the potential problems of the previously widely adopted metrics for learned propensities, and propose balanced-mean-squared-error (BMSE) metric for debiased recommendations. Based on BMSE, we propose IPS-V2 and DR-V2 as the estimators of unbiased loss, and theoretically show that IPS-V2 and DR-V2 have greater propensity balancing and smaller variance without sacrificing additional bias. We further propose a co-training method for learning balanced representation and unbiased prediction. Extensive experiments are conducted on three real-world datasets including a large industrial dataset, and the results show that our approach boosts the balancing property and results in enhanced debiasing performance.

Keyphrases: Debias, Propensity, Recommender System, bias

@booklet{EasyChair:13095,
  author    = {Haoxuan Li and Yanghao Xiao and Chunyuan Zheng and Peng Wu and Peng Cui},
  title     = {Propensity Matters: Measuring and Enhancing Balancing for Recommendation},
  howpublished = {EasyChair Preprint 13095},
  year      = {EasyChair, 2024}}