Sequential Recommender System

Sequential recommender systems aim to predict users' next interactions by analyzing their ordered history of actions, improving personalization and user engagement. Current research focuses on addressing limitations like the long-tail problem (recommending unpopular items), mitigating recency bias, and enhancing efficiency through techniques such as knowledge distillation with pre-trained language models and parameter-efficient fine-tuning of large language models, as well as exploring novel architectures like transformers and recurrent neural networks. These advancements are significant for improving the accuracy and scalability of recommendation systems across various applications, impacting fields like e-commerce, streaming services, and online education.