Beyond Single Sequence: User Correlation Guided Cross-sequence Mixing for Sequential Recommendation

Data augmentation mitigates data sparsity in sequential recommendations by generating new yet effective data. Most existing work focuses on a single original sequence with item-level augmentations. It ignores the correlations between different users (sequences) and struggles to produce diverse yet reasonable new data across sequences, leading to limited performance improvements. Also, the item-level operation may destroy the integrity of the preference knowledge contained in the original sequences, resulting in incomplete preference learning. In this work, we propose a novel user correlation guided cross-sequence mixing plug-in for sequential recommendation (UCMRec). Our core idea is to select the appropriate sequence based on the user correlation graph and perform cross-sequence mixup operations at the representation level to generate high-quality samples. Specifically, we construct a user-user graph based on joint interactions and perform two types of searches to get the candidate users with different correlations. Then, we introduce a mixup operation at the sequence representation level to generate diverse yet reasonable samples across sequences. Furthermore, we propose a topology-aware reweighting module to enable the model to adjust the learning intensity based on preference similarity. Comprehensive experiments demonstrate the superiority of our method.