  Short video applications have attracted billions of users in recent years,
fulfilling their various needs with diverse content. Users usually watch short
videos on many topics on mobile devices in a short period of time, and give
explicit or implicit feedback very quickly to the short videos they watch. The
recommender system needs to perceive users' preferences in real-time in order
to satisfy their changing interests. Traditionally, recommender systems
deployed at server side return a ranked list of videos for each request from
client. Thus it cannot adjust the recommendation results according to the
user's real-time feedback before the next request. Due to client-server
transmitting latency, it is also unable to make immediate use of users'
real-time feedback. However, as users continue to watch videos and feedback,
the changing context leads the ranking of the server-side recommendation system
inaccurate. In this paper, we propose to deploy a short video recommendation
framework on mobile devices to solve these problems. Specifically, we design
and deploy a tiny on-device ranking model to enable real-time re-ranking of
server-side recommendation results. We improve its prediction accuracy by
exploiting users' real-time feedback of watched videos and client-specific
real-time features. With more accurate predictions, we further consider
interactions among candidate videos, and propose a context-aware re-ranking
method based on adaptive beam search. The framework has been deployed on
Kuaishou, a billion-user scale short video application, and improved effective
view, like and follow by 1.28%, 8.22% and 13.6% respectively.
