  Sequential recommendation plays an increasingly important role in many
e-commerce services such as display advertisement and online shopping. With the
rapid development of these services in the last two decades, users have
accumulated a massive amount of behavior data. Richer sequential behavior data
has been proven to be of great value for sequential recommendation. However,
traditional sequential models fail to handle users' lifelong sequences, as
their linear computational and storage cost prohibits them from performing
online inference. Recently, lifelong sequential modeling methods that borrow
the idea of memory networks from NLP are proposed to address this issue.
However, the RNN-based memory networks built upon intrinsically suffer from the
inability to capture long-term dependencies and may instead be overwhelmed by
the noise on extremely long behavior sequences. In addition, as the user's
behavior sequence gets longer, more interests would be demonstrated in it. It
is therefore crucial to model and capture the diverse interests of users. In
order to tackle these issues, we propose a novel lifelong incremental
multi-interest self attention based sequential recommendation model, namely
LimaRec. Our proposed method benefits from the carefully designed
self-attention to identify relevant information from users' behavior sequences
with different interests. It is still able to incrementally update users'
representations for online inference, similarly to memory network based
approaches. We extensively evaluate our method on four real-world datasets and
demonstrate its superior performances compared to the state-of-the-art
baselines.
