Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems either rely on human-annotated rationales to train models for explanation generation or leverage the attention mechanism to extract important text spans from reviews as explanations. The extracted rationales are often confined to an individual review and may fail to identify the implicit features beyond the review text. To avoid the expensive human annotation process and to generate explanations beyond individual reviews, we propose to incorporate a geometric prior learnt from user-item interactions into a variational network which infers latent factors from user-item reviews. The latent factors from an individual user-item pair can be used for both recommendation and explanation generation, which naturally inherit the global characteristics encoded in the prior knowledge. Experimental results on three e-commerce datasets show that our model significantly improves the interpretability of a variational recommender using the Wasserstein distance while achieving performance comparable to existing content-based recommender systems in terms of recommendation behaviours.

翻译：可解释推荐系统能够解释其推荐决策，从而增强用户对系统的信任。大多数可解释推荐系统要么依赖人工标注的理由来训练模型生成解释，要么利用注意力机制从评论中提取重要文本片段作为解释。然而，提取的理由往往局限于单条评论，可能无法识别评论文本之外的隐含特征。为避免昂贵的人工标注过程并生成超越单条评论的解释，我们提出将用户-物品交互中学到的几何先验融入变分网络，该网络从用户-物品评论中推断潜在因子。来自单个用户-物品对的潜在因子可用于推荐和解释生成，这些因子自然继承了先验知识中编码的全局特征。在三个电子商务数据集上的实验结果表明，我们的模型在利用Wasserstein距离显著提升变分推荐器可解释性的同时，在推荐性能方面达到了与现有基于内容的推荐系统相当的水平。