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Explaining Recommender Systems' Performance via User Behaviour Patterns
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DOI：10.1007/978-3-032-11442-6_3.png)
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Abstract
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Recommender systems are widely adopted in digital retail platforms, and stakeholders increasingly demand transparency in how and when they perform reliably. We introduce PS4XRS (Partial Solutions for Explainable Recommender Systems), as a novel XAI tool. Methodology: Using a dataset of user interactions and model performance, we can generate explanations in the form When the user interacts with at least 3 of these groups of items, we expect the model performance to be .... These explanations are obtained via a multi-objective evolutionary algorithm, with objectives based on interpretability, performance and the knowledge from latent item representations derived from the deep recommender system. We performed experiments to determine the most effective parameters for the evolutionary process, and evaluate the trade-offs between explanation complexity and stakeholder usability. Source code for our work can be found athttps://github.com/Giancarlo-Catalano/PSSearch.
Keywords:
Partial Solutions
Recommender Systems
eXplainable Artificial Intelligence
Genetic Algorithms
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Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningIF0
PREAI
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