  The explosively generated micro-videos on content sharing platforms call for
recommender systems to permit personalized micro-video discovery with ease.
Recent advances in micro-video recommendation have achieved remarkable
performance in mining users' current preference based on historical behaviors.
However, most of them neglect the dynamic and time-evolving nature of users'
preference, and the prediction on future micro-videos with historically mined
preference may deteriorate the effectiveness of recommender systems. In this
paper, we propose the DMR framework to explicitly model dynamic multi-trends of
users' current preference and make predictions based on both the history and
future potential trends. We devise the DMR framework, which comprises: 1) the
implicit user network module which identifies sequence fragments from other
users with similar interests and extracts the sequence fragments that are
chronologically behind the identified fragments; 2) the multi-trend routing
module which assigns each extracted sequence fragment into a trend group and
update the corresponding trend vector; 3) the history-future trend prediction
module jointly uses the history preference vectors and future trend vectors to
yield the final click-through-rate. We validate the effectiveness of DMR over
multiple state-of-the-art micro-video recommenders on two publicly available
real-world datasets. Relatively extensive analysis further demonstrate the
superiority of modeling dynamic multi-trend for micro-video recommendation.
