Sustainable transparency on recommender systems: Bayesian ranking of images for explainability
Information Fusion, ISSN: 1566-2535, Vol: 111, Page: 102497
2024
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Most Recent News
Researchers from University of A Coruna Describe Findings in Sustainability Research (Sustainable Transparency On Recommender Systems: Bayesian Ranking of Images for Explainability)
2024 OCT 01 (NewsRx) -- By a News Reporter-Staff News Editor at Economics Daily Report -- Current study results on Sustainability Research have been published.
Article Description
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.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1566253524002756; http://dx.doi.org/10.1016/j.inffus.2024.102497; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85194531178&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1566253524002756; https://dx.doi.org/10.1016/j.inffus.2024.102497
Elsevier BV
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