  Predicting a user's preference in a short anonymous interaction session
instead of long-term history is a challenging problem in the real-life
session-based recommendation, e.g., e-commerce and media stream. Recent
research of the session-based recommender system mainly focuses on sequential
patterns by utilizing the attention mechanism, which is straightforward for the
session's natural sequence sorted by time. However, the user's preference is
much more complicated than a solely consecutive time pattern in the transition
of item choices. In this paper, therefore, we study the item transition pattern
by constructing a session graph and propose a novel model which collaboratively
considers the sequence order and the latent order in the session graph for a
session-based recommender system. We formulate the next item recommendation
within the session as a graph classification problem. Specifically, we propose
a weighted attention graph layer and a Readout function to learn embeddings of
items and sessions for the next item recommendation. Extensive experiments have
been conducted on two benchmark E-commerce datasets, Yoochoose and Diginetica,
and the experimental results show that our model outperforms other
state-of-the-art methods.
