  E-commerce queries are often short and ambiguous. Consequently, query
understanding often uses query rewriting to disambiguate user-input queries.
While using e-commerce search tools, users tend to enter multiple searches,
which we call context, before purchasing. These history searches contain
contextual insights about users' true shopping intents. Therefore, modeling
such contextual information is critical to a better query rewriting model.
However, existing query rewriting models ignore users' history behaviors and
consider only the instant search query, which is often a short string offering
limited information about the true shopping intent.
  We propose an end-to-end context-aware query rewriting model to bridge this
gap, which takes the search context into account. Specifically, our model
builds a session graph using the history search queries and their contained
words. We then employ a graph attention mechanism that models cross-query
relations and computes contextual information of the session. The model
subsequently calculates session representations by combining the contextual
information with the instant search query using an aggregation network. The
session representations are then decoded to generate rewritten queries.
Empirically, we demonstrate the superiority of our method to state-of-the-art
approaches under various metrics. On in-house data from an online shopping
platform, by introducing contextual information, our model achieves 11.6%
improvement under the MRR (Mean Reciprocal Rank) metric and 20.1% improvement
under the HIT@16 metric (a hit rate metric), in comparison with the best
baseline method (Transformer-based model).
