Sequential Recommendation via Adaptive Robust Attention with Multi-dimensional Embeddings
<|reference_start|>Sequential Recommendation via Adaptive Robust Attention with Multi-dimensional Embeddings: Sequential recommendation models have achieved state-of-the-art performance using self-attention mechanism. It has since been found that moving beyond only using item ID and positional embeddings leads to a significant accuracy boost when predicting the next item. In recent literature, it was reported that a multi-dimensional kernel embedding with temporal contextual kernels to capture users' diverse behavioral patterns results in a substantial performance improvement. In this study, we further improve the sequential recommender model's robustness and generalization by introducing a mix-attention mechanism with a layer-wise noise injection (LNI) regularization. We refer to our proposed model as adaptive robust sequential recommendation framework (ADRRec), and demonstrate through extensive experiments that our model outperforms existing self-attention architectures.<|reference_end|>
@article{pang2024sequential,
    title={Sequential Recommendation via Adaptive Robust Attention with
  Multi-dimensional Embeddings},
    author={Linsey Pang, Amir Hossein Raffiee, Wei Liu, Keld Lundgaard},
    journal={arXiv preprint arXiv:2409.05022},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05022},
    primaryClass={cs.IR cs.AI cs.LG}
}