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.

翻译：在建议系统中,利用用户互动历史作为相继信息,取得了很大的绩效改进;然而,在许多在线服务中,用户互动通常按可能共享偏好的会议分组,这要求采用不同于普通顺序代表技术的方法;为此,开发了具有等级结构或不同观点的顺序代表模式,但网络结构相当复杂;在本文件中,我们提出三种方法,通过利用会议信息,同时尽量减少基于BERT的顺序建议模式中的额外参数,改进建议执行情况:使用会议标志,增加会议段嵌入,以及时间意识自觉。我们通过对广泛使用的建议数据集进行实验,展示了拟议方法的可行性。