Search Results for author:
Found 4 papers, 4 papers with code
Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation
Intriguingly, the conclusion drawn from our study is that, certain data augmentation strategies can achieve similar or even superior performance compared with some CL-based methods, demonstrating the potential to significantly alleviate the data sparsity issue with fewer computational overhead.
Attention Calibration for Transformer-based Sequential Recommendation
Our empirical analysis of some representative Transformer-based SR models reveals that it is not uncommon for large attention weights to be assigned to less relevant items, which can result in inaccurate recommendations.
Equivariant Contrastive Learning for Sequential Recommendation
Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e. g., item substitution) and insensitive to mild augmentations (e. g., featurelevel dropout masking).
AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation
Sequential recommendation (SR) aims to model users dynamic preferences from a series of interactions.
