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Session-based recommendations which predict the next action by understandinga user's interaction behavior with items within a relatively short ongoingsession have recently gained increasing popularity. Previous research hasfocused on capturing the dynamics of sequential dependencies from complicateditem transitions in a session by means of recurrent neural networks,self-attention models, and recently, mostly graph neural networks. Despite theplethora of different models relying on the order of items in a session, fewapproaches have been proposed for dealing better with the temporal implicationsbetween interactions. We present Temporal Graph Neural Networks (TempGNN), ageneric framework for capturing the structural and temporal dynamics in complexitem transitions utilizing temporal embedding operators on nodes and edges ondynamic session graphs, represented as sequences of timed events. Extensiveexperimental results show the effectiveness and adaptability of the proposedmethod by plugging it into existing state-of-the-art models. Finally, TempGNNachieved state-of-the-art performance on two real-world e-commerce datasets.

TempGNN: Temporal Graph Neural Networks for Dynamic Session-Based Recommendations