How can recommender systems benefit from large language models: A survey

J Lin, X Dai, Y **, W Liu, B Chen, H Zhang… - ACM Transactions on …, 2023‏ - dl.acm.org
With the rapid development of online services and web applications, recommender systems
(RS) have become increasingly indispensable for mitigating information overload and …

When moe meets llms: Parameter efficient fine-tuning for multi-task medical applications

Q Liu, X Wu, X Zhao, Y Zhu, D Xu, F Tian… - Proceedings of the 47th …, 2024‏ - dl.acm.org
The recent surge in Large Language Models (LLMs) has garnered significant attention
across numerous fields. Fine-tuning is often required to fit general LLMs for a specific …

Large language models for generative recommendation: A survey and visionary discussions

L Li, Y Zhang, D Liu, L Chen - arxiv preprint arxiv:2309.01157, 2023‏ - arxiv.org
Recent years have witnessed the wide adoption of large language models (LLM) in different
fields, especially natural language processing and computer vision. Such a trend can also …

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

Q Wang, J Li, S Wang, Q **ng, 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 …

Recranker: Instruction tuning large language model as ranker for top-k recommendation

S Luo, B He, H Zhao, W Shao, Y Qi, Y Huang… - ACM Transactions on …, 2024‏ - dl.acm.org
Large Language Models (LLMs) have demonstrated remarkable capabilities and have been
extensively deployed across various domains, including recommender systems. Prior …

Llm-esr: Large language models enhancement for long-tailed sequential recommendation

Q Liu, X Wu, Y Wang, Z Zhang, F Tian… - Advances in …, 2025‏ - proceedings.neurips.cc
Sequential recommender systems (SRS) aim to predict users' subsequent choices based on
their historical interactions and have found applications in diverse fields such as e …

Perfedrec++: Enhancing personalized federated recommendation with self-supervised pre-training

S Luo, Y **ao, X Zhang, Y Liu, W Ding… - ACM Transactions on …, 2024‏ - dl.acm.org
Federated recommendation systems employ federated learning techniques to safeguard
user privacy by transmitting model parameters instead of raw user data between user …

A survey of generative search and recommendation in the era of large language models

Y Li, X Lin, W Wang, F Feng, L Pang, W Li, L Nie… - arxiv preprint arxiv …, 2024‏ - arxiv.org
With the information explosion on the Web, search and recommendation are foundational
infrastructures to satisfying users' information needs. As the two sides of the same coin, both …

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 …

Darec: A disentangled alignment framework for large language model and recommender system

X Yang, H **g, Z Zhang, J Wang, H Niu… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Benefiting from the strong reasoning capabilities, Large language models (LLMs) have
demonstrated remarkable performance in recommender systems. Various efforts have been …