2024/09/08 by Linsey Pang, Amir Hossein Raffiee, Pang, Linsey +5
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Recommender Systems and Techniques #Stochastic Gradient Optimization Techniques
paper · pdf · doi:10.48550/arxiv.2409.05022
openalex publication_date 2024/09/08 · openalex created_date 2024/10/22 · openalex updated_date 2026/07/28
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.