Journal of Multimedia Processing and Technologies Qianghuai Jia, Ningyu Zhang, Nengwei Hua 11 1 2020 https://doi.org/10.6025/jmpt/2020/11/1/23-35 http://www.dline.info/jmpt/fulltext/v11n1/jmptv11n1_3.pdf 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 largescale, realworld search logs from a widely used commercial Chinese search engine. Our system has been deployed in ShenMa Search Engine 1 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%.