  Entity recommendation, providing search users with an improved experience via
assisting them in finding related entities for a given query, has become an
indispensable feature of today's search engines. Existing studies typically
only consider the queries with explicit entities. They usually fail to handle
complex queries that without entities, such as "what food is good for cold
weather", because their models could not infer the underlying meaning of the
input text. In this work, we believe that contexts convey valuable evidence
that could facilitate the semantic modeling of queries, and take them into
consideration for entity recommendation. In order to better model the semantics
of queries and entities, we learn the representation of queries and entities
jointly with attentive deep neural networks. We evaluate our approach using
large-scale, real-world search logs from a widely used commercial Chinese
search engine. Our system has been deployed in ShenMa Search Engine and you can
fetch it in UC Browser of Alibaba. Results from online A/B test suggest that
the impression efficiency of click-through rate increased by 5.1% and page view
increased by 5.5%.
