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In order to model the evolution of user preference, we should learn user/itemembeddings based on time-ordered item purchasing sequences, which is defined asSequential Recommendation (SR) problem. Existing methods leverage sequentialpatterns to model item transitions. However, most of them ignore crucialtemporal collaborative signals, which are latent in evolving user-iteminteractions and coexist with sequential patterns. Therefore, we propose tounify sequential patterns and temporal collaborative signals to improve thequality of recommendation, which is rather challenging. Firstly, it is hard tosimultaneously encode sequential patterns and collaborative signals. Secondly,it is non-trivial to express the temporal effects of collaborative signals.  Hence, we design a new framework Temporal Graph Sequential Recommender(TGSRec) upon our defined continuous-time bi-partite graph. We propose a novelTemporal Collaborative Trans-former (TCT) layer in TGSRec, which advances theself-attention mechanism by adopting a novel collaborative attention. TCT layercan simultaneously capture collaborative signals from both users and items, aswell as considering temporal dynamics inside sequential patterns. We propagatethe information learned fromTCTlayerover the temporal graph to unify sequentialpatterns and temporal collaborative signals. Empirical results on five datasetsshow that TGSRec significantly outperforms other baselines, in average up to22.5% and 22.1%absolute improvements in Recall@10and MRR, respectively.

Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer