  Watching micro-videos is becoming a part of public daily life. Usually, user
watching behaviors are thought to be rooted in their multiple different
interests. In the paper, we propose a model named OPAL for micro-video
matching, which elicits a user's multiple heterogeneous interests by
disentangling multiple soft and hard interest embeddings from user
interactions. Moreover, OPAL employs a two-stage training strategy, in which
the pre-train is to generate soft interests from historical interactions under
the guidance of orthogonal hyper-categories of micro-videos and the fine-tune
is to reinforce the degree of disentanglement among the interests and learn the
temporal evolution of each interest of each user. We conduct extensive
experiments on two real-world datasets. The results show that OPAL not only
returns diversified micro-videos but also outperforms six state-of-the-art
models in terms of recall and hit rate.
