  Recently, interactive recommender systems are becoming increasingly popular.
The insight is that, with the interaction between users and the system, (1)
users can actively intervene the recommendation results rather than passively
receive them, and (2) the system learns more about users so as to provide
better recommendation.
  We focus on the single-round interaction, i.e. the system asks the user a
question (Step 1), and exploits his feedback to generate better recommendation
(Step 2). A novel query-based interactive recommender system is proposed in
this paper, where \textbf{personalized questions are accurately generated from
millions of automatically constructed questions} in Step 1, and \textbf{the
recommendation is ensured to be closely-related to users' feedback} in Step 2.
We achieve this by transforming Step 1 into a query recommendation task and
Step 2 into a retrieval task. The former task is our key challenge. We firstly
propose a model based on Meta-Path to efficiently retrieve hundreds of query
candidates from the large query pool. Then an adapted Attention-GRU model is
developed to effectively rank these candidates for recommendation. Offline and
online experiments on Taobao, a large-scale e-commerce platform in China,
verify the effectiveness of our interactive system. The system has already gone
into production in the homepage of Taobao App since Nov. 11, 2018 (see
https://v.qq.com/x/page/s0833tkp1uo.html on how it works online). Our code and
dataset are public in https://github.com/zyody/QueryQR.
