  In product search, users tend to browse results on multiple search result
pages (SERPs) (e.g., for queries on clothing and shoes) before deciding which
item to purchase. Users' clicks can be considered as implicit feedback which
indicates their preferences and used to re-rank subsequent SERPs. Relevance
feedback (RF) techniques are usually involved to deal with such scenarios.
However, these methods are designed for document retrieval, where relevance is
the most important criterion. In contrast, product search engines need to
retrieve items that are not only relevant but also satisfactory in terms of
customers' preferences. Personalization based on users' purchase history has
been shown to be effective in product search. However, this method captures
users' long-term interest, which does not always align with their short-term
interest, and does not benefit customers with little or no purchase history. In
this paper, we study RF techniques based on both long-term and short-term
context dependencies in multi-page product search. We also propose an
end-to-end context-aware embedding model which can capture both types of
context. Our experimental results show that short-term context leads to much
better performance compared with long-term and no context. Moreover, our
proposed model is more effective than state-of-art word-based RF models.
