  Product search has been a crucial entry point to serve people shopping
online. Most existing personalized product models follow the paradigm of
representing and matching user intents and items in the semantic space, where
finer-grained matching is totally discarded and the ranking of an item cannot
be explained further than just user/item level similarity. In addition, while
some models in existing studies have created dynamic user representations based
on search context, their representations for items are static across all search
sessions. This makes every piece of information about the item always equally
important in representing the item during matching with various user intents.
Aware of the above limitations, we propose a review-based transformer model
(RTM) for personalized product search, which encodes the sequence of query,
user reviews, and item reviews with a transformer architecture. RTM conducts
review-level matching between the user and item, where each review has a
dynamic effect according to the context in the sequence. This makes it possible
to identify useful reviews to explain the scoring. Experimental results show
that RTM significantly outperforms state-of-the-art personalized product search
baselines.
