{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sustainable-transparency-in-recommender","title":"Sustainable transparency in Recommender Systems: Bayesian Ranking of Images for Explainability","arxiv_id":"2308.01196","date":"2023-07-27","proceeding":null,"authors":["Jorge Paz-Ruza","Amparo Alonso-Betanzos","Berta Guijarro-Berdiñas","Brais Cancela","Carlos Eiras-Franco"],"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.","url_abs":"https://arxiv.org/abs/2308.01196v3","url_pdf":"https://arxiv.org/pdf/2308.01196v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sustainable-transparency-in-recommender","repo_url":"https://github.com/kominaru/brie","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"explainable-recommendation","task_name":"Explainable Recommendation"},{"task_slug":"explanation-generation","task_name":"Explanation Generation"},{"task_slug":"image-based-recommendation-explainability","task_name":"Image-based Recommendation Explainability"},{"task_slug":"learning-to-rank","task_name":"Learning-To-Rank"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}