  Recommender systems have played a vital role in online platforms due to the
ability of incorporating users' personal tastes. Beyond accuracy, diversity has
been recognized as a key factor in recommendation to broaden user's horizons as
well as to promote enterprises' sales. However, the trading-off between
accuracy and diversity remains to be a big challenge, and the data and user
biases have not been explored yet.
  In this paper, we develop an adaptive learning framework for accurate and
diversified recommendation. We generalize recent proposed bi-lateral branch
network in the computer vision community from image classification to item
recommendation. Specifically, we encode domain level diversity by adaptively
balancing accurate recommendation in the conventional branch and diversified
recommendation in the adaptive branch of a bilateral branch network. We also
capture user level diversity using a two-way adaptive metric learning backbone
network in each branch. We conduct extensive experiments on three real-world
datasets. Results demonstrate that our proposed approach consistently
outperforms the state-of-the-art baselines.
