  How to extract meaningful information in user historical behavior plays a
crucial role in recommendation. User behavior sequence often contains multiple
conceptually distinct items that belong to different item groups and the number
of the item groups is changing over time. It is necessary to learn a dynamic
group of representations according the item groups in a user historical
behavior. However, current works only learns a predefined and fixed number
representations which includes single representation methods and multi
representations methods from the user context that could lead to suboptimal
recommendation quality. In this paper we propose a model that can automatically
and adaptively generates a dynamic group of representations from the user
behavior accordingly. To be specific, AutoRep is composed of an informative
representation construct (IRC) module and a dynamic representations construct
(DRC) module. The IRC module learns the overall sequential characteristics of
user behavior with a bi-directional architecture transformer. The DRC module
dynamically allocate the item in the user behavior into different item groups
and form a dynamic group of representations in a differentiable method. Such
design improves the model recommendation performance. We evaluate the proposed
model on five benchmark datasets. The results show that AutoRep outperforms
representative baselines. Further ablation study has been conducted to deepen
our understandings of AutoRep, including the proposed module IRC and DRC.
