  Session-based recommendation (SBR) is a challenging task, which aims at
recommending next items based on anonymous interaction sequences. Despite the
superior performance of existing methods for SBR, there are still several
limitations: (i) Almost all existing works concentrate on single interest
extraction and fail to disentangle multiple interests of user, which easily
results in suboptimal representations for SBR. (ii) Furthermore, previous
methods also ignore the multi-form temporal information, which is significant
signal to obtain current intention for SBR. To address the limitations
mentioned above, we propose a novel method, called \emph{Temporal aware
Multi-Interest Graph Neural Network} (TMI-GNN) to disentangle multi-interest
and yield refined intention representations with the injection of two level
temporal information. Specifically, by appending multiple interest nodes, we
construct a multi-interest graph for current session, and adopt the GNNs to
model the item-item relation to capture adjacent item transitions,
item-interest relation to disentangle the multi-interests, and interest-item
relation to refine the item representation. Meanwhile, we incorporate
item-level time interval signals to guide the item information propagation, and
interest-level time distribution information to assist the scattering of
interest information. Experiments on three benchmark datasets demonstrate that
TMI-GNN outperforms other state-of-the-art methods consistently.
