  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.
