Can I read AutoDebias: Learning to Debias for Recommendation on EtoBox?

AutoDebias: Learning to Debias for Recommendation by Chen, Jiawei; Dong, Hande; Qiu, Yang; He, Xiangnan; Xin, Xin; Chen, Liang; Lin, Guli; Yang, Keping is a scholarly article available to read on EtoBox.

What is AutoDebias: Learning to Debias for Recommendation about?

Recommender systems rely on user behavior data like ratings and clicks to build personalization model. However, the collected data is observational rather than experimental, causing various biases in the data which significantly affect the learned model. Most existing work for recommendation debiasing, such as the inverse propensity scoring and imputation approaches, focuses on one or two specific biases, lacking the universal capacity that can account for mixed or even unknown biases in the data. Towards this research gap, we first analyze the origin of biases from the perspective of \textit{risk discrepancy} that represents the difference between the expectation empirical risk and the true risk. Remarkably, we derive a general learning framework that well summarizes most existing debiasing strategies by specifying some parameters of the general framework. This provides a valuable opportunity to develop a universal solution for debiasing, e.g., by learning the debiasing parameters from data. However, the training data lacks important signal of how the data is biased and what the unbiased data looks like. To move this idea forward, we propose \textit{AotoDebias} that leverages anot

Author

Chen, Jiawei; Dong, Hande; Qiu, Yang; He, Xiangnan; Xin, Xin; Chen, Liang; Lin, Guli; Yang, Keping

Published

2021

Language

EN