Cross-domain sequential recommendation (CDSR) aims to utilize users' interactions across multiple domains to alleviate the problem of interaction sparsity that is prevalent in web platforms, thereby providing more accurate personalized recommendations. Although current CDSR methods have made some progress, they suffer from two main limitations: (i) assuming uniformly distributed interactions over time; and (ii) neglecting temporal influences during cross-domain transfer. In order to address the above issues, we propose a novel Time-Aware Cross-Domain Sequential Recommendation framework (TA-CDSR ). First, we design a time-sensitive attention which captures user preferences over time by decoupling interaction sequences and time sequences. Second, we propose a time-guided preference generator that can reconstruct the lacking interactions in the target domain by taking the source domain interactions time as guidance information. Finally, we design a multi-scale time windows based domain transfer module, which can dynamically identify the temporal interaction density and thus adaptively assign the weights of cross-domain information. Extensive experiments on three real-world datasets indicate that TA-CDSR achieves competitive time complexity while outperforming other baselines.

As a typical spatio-temporal series prediction task, traffic flow prediction has found wide application in Intelligent Transportation Systems (ITS). Despite some progress, several unresolved issues persist. Many existing works calculate the dependencies between nodes based on stable long-term traffic data. However, the short-term dependencies are dynamically changing over time, and neglecting them would cause a decrease in predictive performance. In this paper, we propose a novel Dynamic Graph Convolution and Spatio-Temporal Self-Attention (DGSTA) network for traffic flow prediction. Specifically, considering the large amount of short-term and the dynamic dependencies between nodes, we design a new dynamic graph convolution module, which generates adjacency matrices for each time step in a day to dynamically capture the changing short-term dependencies. Additionally, we utilize a multi-head spatio-temporal self-attention module to respectively extract static spatial and temporal correlations between nodes. Furthermore, we design a sequential embedding to explicitly model the long-term correlation between nodes. Extensive experiments conducted on three real-world datasets demonstrate that DGSTA exhibits high competitiveness. The code and data are available at https://github.com/lzmmm30/DGSTA.