Related papers: Adversarial Counterfactual Learning and Evaluation for Recommender System

Adversarial Counterfactual Learning and Evaluation for Recommender System

URL: http://arxiv.org/abs/2012.02295v1
Date: Sun, 8 Nov 2020 00:40:51 GMT
Title: Adversarial Counterfactual Learning and Evaluation for Recommender System
Authors: Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Abstract summary: We show in theory that applying supervised learning to detect user preferences may end up with inconsistent results in the absence of exposure information. We propose a principled solution by introducing a minimax empirical risk formulation.
Score: 33.44276155380476
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The feedback data of recommender systems are often subject to what was exposed to the users; however, most learning and evaluation methods do not account for the underlying exposure mechanism. We first show in theory that applying supervised learning to detect user preferences may end up with inconsistent results in the absence of exposure information. The counterfactual propensity-weighting approach from causal inference can account for the exposure mechanism; nevertheless, the partial-observation nature of the feedback data can cause identifiability issues. We propose a principled solution by introducing a minimax empirical risk formulation. We show that the relaxation of the dual problem can be converted to an adversarial game between two recommendation models, where the opponent of the candidate model characterizes the underlying exposure mechanism. We provide learning bounds and conduct extensive simulation studies to illustrate and justify the proposed approach over a broad range of recommendation settings, which shed insights on the various benefits of the proposed approach.

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