Explaining recommendation systems through contrapositive perturbations

Jean-Francois Ton, Yegor Klochkov, Yang Liu, Hang Li

Primary Area: visualization or interpretation of learned representations

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Keywords: Explanations, XAI

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TL;DR: We propose a new way to efficiently compute explanations for recommender systems through contrapositive logic.

Abstract:

Recommender systems are widely used to help users discover new items online. A popular method for recommendations is factorization models, which predict a user's preference for an item based on latent factors derived from their interaction history. However, explaining why a particular item was recommended to a user is challenging, and current approaches such as counterfactual explanations can be computationally expensive. In this paper, we propose a new approach called contrapositive explanations that leverages a different logical structure to counterfactual explanations. We show how contrapositive explanations can be used to explain recommendation systems by finding the minimum change that would have resulted in a different recommendation. Specifically, we present a methodology that focuses on finding an explanation in the form of "Because the user interacted with item, j we recommend item i to the user," which is easier to compute and find compared to traditional counterfactual approaches which aim at "Because the user $\textbf{did not}$
interacted with item j, we $\textbf{did not}$ recommend item i to the user,". We evaluate our approach on several real-world datasets and show that it provides effective and efficient explanations compared to other existing methods.

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Submission Number: 5544