  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.
