Attention Based Sequential Recommendation

Attention-based sequential recommendation systems aim to predict users' next actions (e.g., purchases, clicks) by learning patterns from their sequential interactions, leveraging the power of attention mechanisms to focus on the most relevant past items. Current research emphasizes improving efficiency (e.g., through architecture search and pruning), robustness (e.g., via advanced reinforcement learning objectives and contrastive learning), and the incorporation of diverse data modalities (e.g., images, text, and user attributes) to enhance prediction accuracy and personalization. These advancements are significant for improving the performance and scalability of recommender systems across various applications, leading to more effective and personalized user experiences.