  Existing explanation models generate only text for recommendations but still
struggle to produce diverse contents. In this paper, to further enrich
explanations, we propose a new task named personalized showcases, in which we
provide both textual and visual information to explain our recommendations.
Specifically, we first select a personalized image set that is the most
relevant to a user's interest toward a recommended item. Then, natural language
explanations are generated accordingly given our selected images. For this new
task, we collect a large-scale dataset from Google Local (i.e.,~maps) and
construct a high-quality subset for generating multi-modal explanations. We
propose a personalized multi-modal framework which can generate diverse and
visually-aligned explanations via contrastive learning. Experiments show that
our framework benefits from different modalities as inputs, and is able to
produce more diverse and expressive explanations compared to previous methods
on a variety of evaluation metrics.
