Dual Contrastive Learning and Dual Bi-directional Transformer Encoders for Sequential Recommendations

Sequential recommendation is a hot research in recommender systems, which Transformer-based models have achieved state-of-the-art performance. However, existing methods lack consideration of historical-level information, leading to ineffective modeling of user preference. To utilize history-level information and enhance item sequence representation infused with historical information, we design a model based on dual bi-directional Transformer encoders and dual Contrastive Learning named DBT4Rec. We first design a dual bi-directional Transformer encoder to capture the relationship between item-level sequences and history-level sequences, then design a dual Contrastive Learning to enhance item sequence representation integrated with history information. Finally, experiment results on three public benchmark datasets show that our model outperforms state-of-the-art models for sequential recommendation.