  Sequential recommendation systems alleviate the problem of information
overload, and have attracted increasing attention in the literature. Most prior
works usually obtain an overall representation based on the user's behavior
sequence, which can not sufficiently reflect the multiple interests of the
user. To this end, we propose a novel method called PIMI to mitigate this
issue. PIMI can model the user's multi-interest representation effectively by
considering both the periodicity and interactivity in the item sequence.
Specifically, we design a periodicity-aware module to utilize the time interval
information between user's behaviors. Meanwhile, an ingenious graph is proposed
to enhance the interactivity between items in user's behavior sequence, which
can capture both global and local item features. Finally, a multi-interest
extraction module is applied to describe user's multiple interests based on the
obtained item representation. Extensive experiments on two real-world datasets
Amazon and Taobao show that PIMI outperforms state-of-the-art methods
consistently.
