Towards next-generation llm-based recommender systems: A survey and beyond

Q Wang, J Li, S Wang, Q Xing, R Niu, H Kong… - arXiv preprint arXiv …, 2024 - arxiv.org
Large language models (LLMs) have not only revolutionized the field of natural language
processing (NLP) but also have the potential to bring a paradigm shift in many other fields …

Large Language Model Enhanced Recommender Systems: Taxonomy, Trend, Application and Future

Q Liu, X Zhao, Y Wang, Y Wang, Z Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
Large Language Model (LLM) has transformative potential in various domains, including
recommender systems (RS). There have been a handful of research that focuses on …

Combining association-rule-guided sequence augmentation with listwise contrastive learning for session-based recommendation

X Lu, J Wu - Information Processing & Management, 2025 - Elsevier
Sequence augmentation based contrastive learning (SACL) plays a critical role in user
behavior modeling towards sequential recommendation tasks. However, SACL cannot work …

A Practice-Friendly Two-Stage LLM-Enhanced Paradigm in Sequential Recommendation

D Liu, S Xian, X Lin, X Zhang, H Zhu, Y Fang… - arXiv preprint arXiv …, 2024 - arxiv.org
The training paradigm integrating large language models (LLM) is gradually reshaping
sequential recommender systems (SRS) and has shown promising results. However, most …