Bridging Time and Domains: A Time-aware Framework for Cross-Domain Sequential Recommendation

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.