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CLEAR: Causal Explanations from Attention in Neural Recommenders

2022-10-07 07:40:29
Shami Nisimov, Raanan Y. Rohekar, Yaniv Gurwicz, Guy Koren, Gal Novik
arXiv_AI
       
Abstract

We present CLEAR, a method for learning session-specific causal graphs, in the possible presence of latent confounders, from attention in pre-trained attention-based recommenders. These causal graphs describe user behavior, within the context captured by attention, and can provide a counterfactual explanation for a recommendation. In essence, these causal graphs allow answering "why" questions uniquely for any specific session. Using empirical evaluations we show that, compared to naively using attention weights to explain input-output relations, counterfactual explanations found by CLEAR are shorter and an alternative recommendation is ranked higher in the original top-k recommendations.

Abstract (translated)

URL

https://arxiv.org/abs/2210.10621

PDF

https://arxiv.org/pdf/2210.10621.pdf

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