  In recommendation systems, utilizing the user interaction history as
sequential information has resulted in great performance improvement. However,
in many online services, user interactions are commonly grouped by sessions
that presumably share preferences, which requires a different approach from
ordinary sequence representation techniques. To this end, sequence
representation models with a hierarchical structure or various viewpoints have
been developed but with a rather complex network structure. In this paper, we
propose three methods to improve recommendation performance by exploiting
session information while minimizing additional parameters in a BERT-based
sequential recommendation model: using session tokens, adding session segment
embeddings, and a time-aware self-attention. We demonstrate the feasibility of
the proposed methods through experiments on widely used recommendation
datasets.
