Streaming session-based recommendation (SSR) is a challenging task that requires the recommender system to do the session-based recommendation (SR) in the streaming scenario. In the real-world applications of e-commerce and social media, a sequence of user-item interactions generated within a certain period are grouped as a session, and these sessions consecutively arrive in the form of streams. Most of the recent SR research has focused on the static setting where the training data is first acquired and then used to train a session-based recommender model. They need several epochs of training over the whole dataset, which is infeasible in the streaming setting. Besides, they can hardly well capture long-term user interests because of the neglect or the simple usage of the user information. Although some streaming recommendation strategies have been proposed recently, they are designed for streams of individual interactions rather than streams of sessions. In this paper, we propose a Global Attributed Graph (GAG) neural network model with a Wasserstein reservoir for the SSR problem. On one hand, when a new session arrives, a session graph with a global attribute is constructed based on the current session and its associate user. Thus, the GAG can take both the global attribute and the current session into consideration to learn more comprehensive representations of the session and the user, yielding a better performance in the recommendation. On the other hand, for the adaptation to the streaming session scenario, a Wasserstein reservoir is proposed to help preserve a representative sketch of the historical data. Extensive experiments on two real-world datasets have been conducted to verify the superiority of the GAG model compared with the state-of-the-art methods.

中文翻译：

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GAG：用于基于会话的流式推荐的全局属性图神经网络

基于流会话的推荐（SSR）是一项具有挑战性的任务，需要推荐系统在流场景中进行基于会话的推荐（SR）。在电子商务和社交媒体的实际应用中，一定时期内产生的一系列用户-项目交互被分组为一个会话，这些会话以流的形式连续到达。大多数最近的 SR 研究都集中在静态设置上，其中首先获取训练数据，然后用于训练基于会话的推荐模型。他们需要对整个数据集进行几个时期的训练，这在流媒体设置中是不可行的。此外，由于对用户信息的忽视或简单使用，它们很难很好地捕捉到长期的用户兴趣。尽管最近已经提出了一些流媒体推荐策略，但它们是为个人交互流而不是会话流而设计的。在本文中，我们针对 SSR 问题提出了一个具有 Wasserstein 水库的全局属性图 (GAG) 神经网络模型。一方面，当新会话到来时，基于当前会话及其关联用户构建具有全局属性的会话图。因此，GAG 可以同时考虑全局属性和当前会话，以学习更全面的会话和用户表示，从而在推荐中产生更好的性能。另一方面，为了适应流会话场景，提出了一个 Wasserstein 水库来帮助保存历史数据的代表性草图。