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

• URL: http://arxiv.org/abs/2308.01196v2
• Date: Thu, 21 Dec 2023 11:27:00 GMT
• Title: Sustainable Transparency in Recommender Systems: Bayesian Ranking of Images for Explainability
• Authors: Jorge Paz-Ruza, Amparo Alonso-Betanzos, Berta Guijarro-Berdi\~nas, Brais Cancela, Carlos Eiras-Franco
• Abstract summary: Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products. personalized explanations have emerged as a solution, offering justifications for recommendations. BRIE is a novel model where we leverage Pairwise Ranking to enhance the training process, allowing us to consistently outperform state-of-the-art models in six real-world datasets.
• Score: 5.499796332553708
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 CO${_2}$ emissions by up to 75% in training and inference.

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