  Session-based Recommendation (SBR) is to predict users' next interested items
based on their previous browsing sessions. Existing methods model sessions as
graphs or sequences to estimate user interests based on their interacted items
to make recommendations. In recent years, graph-based methods have achieved
outstanding performance on SBR. However, none of these methods consider
temporal information, which is a crucial feature in SBR as it indicates
timeliness or currency. Besides, the session graphs exhibit a hierarchical
structure and are demonstrated to be suitable in hyperbolic geometry. But few
papers design the models in hyperbolic spaces and this direction is still under
exploration. In this paper, we propose Time-aware Hyperbolic Graph Attention
Network (TA-HGAT) - a novel hyperbolic graph neural network framework to build
a session-based recommendation model considering temporal information. More
specifically, there are three components in TA-HGAT. First, a hyperbolic
projection module transforms the item features into hyperbolic space. Second,
the time-aware graph attention module models time intervals between items and
the users' current interests. Third, an evolutionary loss at the end of the
model provides an accurate prediction of the recommended item based on the
given timestamp. TA-HGAT is built in a hyperbolic space to learn the
hierarchical structure of session graphs. Experimental results show that the
proposed TA-HGAT has the best performance compared to ten baseline models on
two real-world datasets.
