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arXiv · 2210.10621

CLEAR: Causal Explanations from Attention in Neural Recommenders

Shami Nisimov·Raanan Y. Rohekar·Yaniv Gurwicz·Guy Koren·Gal Novik
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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.

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Shami Nisimov, Raanan Y. Rohekar, Yaniv Gurwicz, Guy Koren, Gal Novik. 2022-10-07. CLEAR: Causal Explanations from Attention in Neural Recommenders. https://arxiv.org/abs/2210.10621

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