  Product search serves as an important entry point for online shopping. In
contrast to web search, the retrieved results in product search not only need
to be relevant but also should satisfy customers' preferences in order to
elicit purchases. Previous work has shown the efficacy of purchase history in
personalized product search. However, customers with little or no purchase
history do not benefit from personalized product search. Furthermore,
preferences extracted from a customer's purchase history are usually long-term
and may not always align with her short-term interests. Hence, in this paper,
we leverage clicks within a query session, as implicit feedback, to represent
users' hidden intents, which further act as the basis for re-ranking subsequent
result pages for the query. It has been studied extensively to model user
preference with implicit feedback in recommendation tasks. However, there has
been little research on modeling users' short-term interest in product search.
We study whether short-term context could help promote users' ideal item in the
following result pages for a query. Furthermore, we propose an end-to-end
context-aware embedding model which can capture long-term and short-term
context dependencies. Our experimental results on the datasets collected from
the search log of a commercial product search engine show that short-term
context leads to much better performance compared with long-term and no
context. Our results also show that our proposed model is more effective than
word-based context-aware models.
