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 visual content created by the users is one particularly promising option, showing a potential to maximize transparency and user trust. Existing models for explaining recommendations in this context face limitations: sustainability has been a critical concern, as they often require substantial computational resources, leading to significant carbon emissions comparable to the Recommender Systems where they would be integrated. Moreover, most models employ surrogate learning goals that do not align with the objective of ranking the most effective personalized explanations for a given recommendation, leading to a suboptimal learning process and larger model sizes. To address these limitations, we present BRIE, a novel model designed to tackle the existing challenges by adopting a more adequate learning goal based on Bayesian Pairwise Ranking, enabling it to achieve consistently superior performance than state-of-the-art models in six real-world datasets, while exhibiting remarkable efficiency, emitting up to 75% less CO${_2}$ during training and inference with a model up to 64 times smaller than previous approaches.

翻译：推荐系统已成为现代社会不可或缺的一部分，通常引导用户获取相关内容或产品，并对用户和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，为推荐提供合理性说明。在现有的个性化解释生成方法中，利用用户创建的视觉内容是特别有前景的选择，展现出最大化透明度和用户信任的潜力。目前用于解释此类推荐内容的模型存在局限性：可持续性一直是一个关键问题，因为它们通常需要大量计算资源，导致与所集成的推荐系统相当巨大的碳排放量。此外，大多数模型采用替代性学习目标，这与为给定推荐排序最有效的个性化解释的目标不一致，导致学习过程欠优和模型尺寸偏大。为解决这些局限性，我们提出了BRIE模型，该模型采用基于贝叶斯成对排序（Bayesian Pairwise Ranking）的更恰当的学习目标来应对现有挑战，在六个真实数据集上始终取得优于最先进模型的性能，同时展现出卓越的效率——训练和推理过程中的二氧化碳排放量减少高达75%，模型体积比先前方法缩小多达64倍。