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.
翻译:建议系统为用户提供了最新的在线大量信息的适当限制。基于会议的建议是建议系统的一个子领域,它试图通过解释由一系列项目组成的会议来建议项目。最近,将用户信息纳入这些会议的研究正在取得进展。然而,很难产生高质量的用户代表,包括用户在会议上的陈述。在本文件中,我们考虑了通过不同主题的注意网络会议产生的图表中的各种关系。还存在制约因素,迫使用户代表考虑在会议上提出的用户偏好。它试图通过在培训过程中进一步优化来提高业绩。拟议的模型在各种现实世界数据集方面优于其他方法。