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Sustainable transparency on recommender systems: Bayesian ranking of images for explainability

Information Fusion, ISSN: 1566-2535, Vol: 111, Page: 102497
2024
  • 1
    Citations
  • 0
    Usage
  • 9
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    1
  • Captures
    9
  • Mentions
    1
    • News Mentions
      1
      • News
        1

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.

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