  Product search is one of the most popular methods for customers to discover
products online. Most existing studies on product search focus on developing
effective retrieval models that rank items by their likelihood to be purchased.
They, however, ignore the problem that there is a gap between how systems and
customers perceive the relevance of items. Without explanations, users may not
understand why product search engines retrieve certain items for them, which
consequentially leads to imperfect user experience and suboptimal system
performance in practice. In this work, we tackle this problem by constructing
explainable retrieval models for product search. Specifically, we propose to
model the "search and purchase" behavior as a dynamic relation between users
and items, and create a dynamic knowledge graph based on both the
multi-relational product data and the context of the search session. Ranking is
conducted based on the relationship between users and items in the latent
space, and explanations are generated with logic inferences and entity soft
matching on the knowledge graph. Empirical experiments show that our model,
which we refer to as the Dynamic Relation Embedding Model (DREM), significantly
outperforms the state-of-the-art baselines and has the ability to produce
reasonable explanations for search results.
