  Candidate retrieval is a fundamental issue in recommendation system. Given
user's recommendation request, relevant candidates need to be retrieved in
realtime for subsequent ranking operations. Considering that the retrieval
operation is conducted over considerable items, it has to be both precise and
scalable so that high-quality candidates can be acquired within tolerable
latency. Unfortunately, conventional methods would trade off precision for high
running efficiency, which leads to inferior retrieval quality. In contrast,
those deep learning-based approaches can be highly accurate in identifying
relevant items; yet, they are unsuitable for candidate retrieval due to their
inherent limitation on scalability.
  In this work, a novel framework is proposed to address the above challenges.
The underlying intuition is to rely on a well-trained ranking model for the
supervision of an efficient retrieval model, such that it will unify the
scalability and precision as a whole. We have implemented our conceptual
framework and made comprehensive evaluation for it, where promising results are
achieved against representative baselines.
  Our work is undergoing a anonymous review, and it will soon be released after
the notification. If you're also interested in this problem, please feel free
to contact us.
