论文标题
用于基于个性化会话的建议的异质图神经网络具有用户会议约束
Heterogeneous Graph Neural Network for Personalized Session-Based Recommendation with User-Session Constraints
论文作者
论文摘要
推荐系统为用户提供了最新在线大量信息的适当限制。基于会话的建议是推荐系统的子区域，试图通过解释由项目序列组成的会话来推荐项目。最近，在这些会话中包括用户信息的研究是进步。但是，很难生成包括用户生成的会话表示的高质量用户表示。在本文中，我们考虑了通过异质注意网络创建的图表中的各种关系。约束还迫使用户表示会考虑会话中介绍的用户偏好。它试图通过在培训过程中的其他优化来提高性能。所提出的模型在各种现实世界数据集上的其他方法优于其他方法。
The recommendation system provides users with an appropriate limit of recent online large amounts of information. Session-based recommendation, a sub-area of recommender systems, attempts to recommend items by interpreting sessions that consist of sequences of items. Recently, research to include user information in these sessions is progress. However, it is difficult to generate high-quality user representation that includes session representations generated by user. In this paper, we consider various relationships in graph created by sessions through Heterogeneous attention network. Constraints also force user representations to consider the user's preferences presented in the session. It seeks to increase performance through additional optimization in the training process. The proposed model outperformed other methods on various real-world datasets.
