Session-based recommendation aims to predict user's next behavior from current session and previous anonymous sessions. Capturing long-range dependencies between items is a vital challenge in session-based recommendation. A novel approach is proposed for session-based recommendation with self-attention networks (SR-SAN) as a remedy. The self-attention networks (SAN) allow SR-SAN capture the global dependencies among all items of a session regardless of their distance. In SR-SAN, a single item latent vector is used to capture both current interest and global interest instead of session embedding which is composed of current interest embedding and global interest embedding. Some experiments have been performed on some open benchmark datasets. Experimental results show that the proposed method outperforms some state-of-the-arts by comparisons.
翻译:以会议为基础的建议旨在预测用户从本届会议和以往匿名会议产生的下一个行为。在届会基础上的建议中,项目之间的长距离依赖性是一个重大挑战。提出了一种新颖的办法,以会议为基础提出建议,以自控网络(SR-SAN)作为补救措施。自控网络(SAN)允许SR-SAN在届会的所有项目之间捕捉全球依赖性,而不论其距离如何。在SR-SAN中,单个项目潜伏矢量被用来捕捉当前利益和全球利益,而不是由当前利益嵌入和全球利益嵌入组成的届会。一些实验已经对某些开放的基准数据集进行了实验。实验结果显示,通过比较,拟议的方法超越了某些最新特点。