Research Progress on Sequence Recommendation Based on Deep Learning and Large Language Model - XU Fengru, LI Bohan, XU Shuai (Jisuanji kexue yu tansuo)
The recommendation system aims to solve the problem of overloading in the information retrieval system, and is committed to recommending personalized interest to users. The behavior of human interaction with the system has a certain order. When providing recommendations, the order needs to be taken into consideration. This is the sequence recommendation system. The sequence recommendation system analyzes user behavior sequences, captures the dynamic changes of user preferences, and provides accurate personalized recommendation services for many fields such as e-commerce, social media and online videos. This paper provides an overview of the current research progress in sequential recommendation systems and explores their significance and application potential in the field of personalized recommendation. Firstly, the research problem of sequential recommendation is defined, and the core objectives and challenges of recommendation sequences are clarified. Next, the main techniques in sequential recommendation are summarized in detail, including: traditional methods based on Markov chains, which model user behavior sequences by relying on state transition probabilities; deep learning-driven methods, which utilize neural network models to capture long-term dependencies and complex patterns; hybrid models, which combine multiple algorithms to enhance the accuracy and robustness of recommendation systems; and emerging methods based on large language models, which improve the understanding of user behavior and recommendation content through the integration of pre-trained large language models. Finally, the future research directions are prospected, with emphasis on the importance of context perception, multimodal fusion, causal inference, specific large language models of vertical fields, alleviating hallucinations, etc.