  Product retrieval systems have served as the main entry for customers to
discover and purchase products online. With increasing concerns on the
transparency and accountability of AI systems, studies on explainable
information retrieval has received more and more attention in the research
community. Interestingly, in the domain of e-commerce, despite the extensive
studies on explainable product recommendation, the studies of explainable
product search is still in an early stage. In this paper, we study how to
construct effective explainable product search by comparing model-agnostic
explanation paradigms with model-intrinsic paradigms and analyzing the
important factors that determine the performance of product search
explanations. We propose an explainable product search model with
model-intrinsic interpretability and conduct crowdsourcing to compare it with
the state-of-the-art explainable product search model with model-agnostic
interpretability. We observe that both paradigms have their own advantages and
the effectiveness of search explanations on different properties are affected
by different factors. For example, explanation fidelity is more important for
user's overall satisfaction on the system while explanation novelty may be more
useful in attracting user purchases. These findings could have important
implications for the future studies and design of explainable product search
engines.
