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.

翻译：推荐系统在当今世界已变得至关重要，通常会引导用户获取相关内容或商品，并对用户及公民的决策产生重大影响。然而，确保这些系统的透明度和用户信任度仍是一项挑战；个性化解释作为一种解决方案应运而生，为推荐内容提供理由。在现有的个性化解释生成方法中，利用用户已有的视觉内容是一种最大化透明度和用户信任度的有前景方案。尽管采用高度优化的架构，但遵循此方法的最先进模型使用了替代学习任务，未能有效建模将图像排序作为给定推荐解释的目标；这会导致训练过程次优且计算成本高昂，若不牺牲模型性能则难以降低。本文提出BRIE模型，创新性地利用贝叶斯成对排序优化训练过程，在六个真实世界数据集上持续超越最先进模型，同时将模型规模降低64倍，并将训练和推理过程中的CO₂排放减少高达75%。