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.

为了解决推荐系统中透明性和用户信任的问题，研究人员提出了一种名为 BRIE 的新模型，通过采用基于贝叶斯对比排序的更适当的学习目标，实现比现有模型更卓越的性能，同时具有较高的效率。