PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems

Abstract

Interpretable explanations for recommender systems and other machine learning models are crucial to gain user trust. Prior works that have focused on paths connecting users and items in a heterogeneous network have several limitations, such as discovering relationships rather than true explanations, or disregarding other users' privacy. In this work, we take a fresh perspective, and present PRINCE: a provider-side mechanism to produce tangible explanations for end-users, where an explanation is defined to be a set of minimal actions performed by the user that, if removed, changes the recommendation to a different item. Given a recommendation, PRINCE uses a polynomial-time optimal algorithm for finding this minimal set of a user's actions from an exponential search space, based on random walks over dynamic graphs. Experiments on two real-world datasets show that PRINCE provides more compact explanations than intuitive baselines, and insights from a crowdsourced user-study demonstrate the viability of such action-based explanations. We thus posit that PRINCE produces scrutable, actionable, and concise explanations, owing to its use of counterfactual evidence, a user's own actions, and minimal sets, respectively.

arXiv commentWSDM 2020, 9 pages

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Cited by in corpus (10)

• Counterfactual Explainable Recommendation
• Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation
• A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
• Causal Collaborative Filtering
• Improving Visualization Interpretation Using Counterfactuals
• Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications
• Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box
• CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
• DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models
• From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems