Hybrid Sequence Augmentation and Optimized Contrastive Loss Recommendation

To address the issues of relevance and diversity imbalance in the augmented data and the shortcomings of existing loss functions, this study proposes a recommendation algorithm based on hybrid sequence augmentation and optimized contrastive loss. First, two new data augmentation operators are designed and combined with the existing operators to form a more diversified augmentation strategy. This approach better balances the relevance and diversity of the augmented data, ensuring that the model can make more accurate recommendations when facing various scenarios. Additionally, to optimize the training process of the model, this study also introduces an improved loss function. Unlike the traditional cross-entropy loss, this loss function introduces a temporal accumulation term before calculating the cross-entropy loss, integrating the advantages of binary cross-entropy loss. This overcomes the limitation of traditional methods, which apply cross-entropy loss only at the last timestamp of the sequence, thereby improving the model's accuracy and stability. Experiments on the Beauty, Sports, Yelp, and Home datasets show significant improvements in the Hit@10 and NDCG@10 metrics, demonstrating the effectiveness of the recommendation model based on hybrid sequence augmentation and optimized contrastive loss. Specifically, the Hit metric, which reflects model accuracy, improves by 8.64%, 13.07%, 5.92%, and 19.28% respectively on these four datasets. The NDCG metric, which measures ranking quality, increases by 15.60%, 19.01%, 9.66%, and 20.31% respectively.

@article{Li2025,
  title     = {Hybrid Sequence Augmentation and Optimized Contrastive Loss Recommendation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Minghui Li and Xiaodong Cai},
  doi       = {10.14569/IJACSA.2025.0160550},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160550}
}