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arXiv · 2308.01196

Sustainable transparency in Recommender Systems: Bayesian Ranking of Images for Explainability

Jorge Paz-Ruza·Amparo Alonso-Betanzos·Berta Guijarro-Berdiñas·Brais Cancela·Carlos Eiras-Franco
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Abstract

Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products, and having a large influence over the decisions of users and citizens. However, ensuring transparency and user trust in these systems remains a challenge; personalized explanations have emerged as a solution, offering justifications for recommendations. Among the existing approaches for generating personalized explanations, using existing visual content created by users is a promising option to maximize transparency and user trust. State-of-the-art models that follow this approach, despite leveraging highly optimized architectures, employ surrogate learning tasks that do not efficiently model the objective of ranking images as explanations for a given recommendation; this leads to a suboptimal training process with high computational costs that may not be reduced without affecting model performance. This work presents BRIE, a novel model where we leverage Bayesian Pairwise Ranking to enhance the training process, allowing us to consistently outperform state-of-the-art models in six real-world datasets while reducing its model size by up to 64 times and its CO2 emissions by up to 75% in training and inference.

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Jorge Paz-Ruza, Amparo Alonso-Betanzos, Berta Guijarro-Berdiñas, Brais Cancela, Carlos Eiras-Franco. 2023-07-27. Sustainable transparency in Recommender Systems: Bayesian Ranking of Images for Explainability. https://doi.org/10.1016/j.inffus.2024.102497

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