  Recently, sequential recommendation systems are important in solving the
information overload in many online services. Current methods in sequential
recommendation focus on learning a fixed number of representations for each
user at any time, with a single representation or multi representations for the
user. However, when a user is exploring items on an e-commerce recommendation
system, the number of this user's hobbies may change overtime (e.g.
increase/reduce one more interest), affected by the user's evolving self needs.
Moreover, different users may have various number of interests. In this paper,
we argue that it is meaningful to explore a personalized dynamic number of user
interests, and learn a dynamic group of user interest representations
accordingly. We propose a sequential model with dynamic number of
representations for recommendation systems (RDRSR). Specifically, RDRSR is
composed of a dynamic interest discriminator (DID) module and a dynamic
interest allocator (DIA) module. The DID module explores the number of a user's
interests by learning the overall sequential characteristics with
bi-directional self-attention and Gumbel-Softmax. The DIA module make the
historical clicked items into a group of item groups and constructs user's
dynamic interest representation. Additionally, experiments on the real-world
datasets demonstrates our model's effectiveness.
