Sustainable transparency in Recommender Systems: Bayesian Ranking of Images for Explainability
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arXiv
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| Format: | Preprint |
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2023
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| author | Paz-Ruza, Jorge Alonso-Betanzos, Amparo Guijarro-Berdiñas, Berta Cancela, Brais Eiras-Franco, Carlos |
| author_facet | Paz-Ruza, Jorge Alonso-Betanzos, Amparo Guijarro-Berdiñas, Berta Cancela, Brais Eiras-Franco, Carlos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_01196 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Sustainable transparency in Recommender Systems: Bayesian Ranking of Images for Explainability Paz-Ruza, Jorge Alonso-Betanzos, Amparo Guijarro-Berdiñas, Berta Cancela, Brais Eiras-Franco, Carlos Information Retrieval Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Sustainable transparency in Recommender Systems: Bayesian Ranking of Images for Explainability |
| topic | Information Retrieval Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2308.01196 |