Knowledge plugins: Enhancing large language models for domain-specific recommendations

A Vats, V Jain, R Raja, A Chadha - arXiv preprint arXiv:2402.18590, 2024 - arxiv.org

The paper underscores the significance of Large Language Models (LLMs) in reshaping
recommender systems, attributing their value to unique reasoning abilities absent in …

被引用次数：17 相关文章所有 5 个版本

[PDF] arxiv.org

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 …

被引用次数：3 相关文章所有 2 个版本

[PDF] arxiv.org

Prompting large language models for recommender systems: A comprehensive framework and empirical analysis

L Xu, J Zhang, B Li, J Wang, M Cai, WX Zhao… - arXiv preprint arXiv …, 2024 - arxiv.org

Recently, large language models such as ChatGPT have showcased remarkable abilities in
solving general tasks, demonstrating the potential for applications in recommender systems …

被引用次数：40 相关文章所有 2 个版本

[PDF] arxiv.org

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

J Lin, X Dai, Y Xi, W Liu, B Chen, H Zhang, Y Liu… - arXiv preprint arXiv …, 2023 - arxiv.org

With the rapid development of online services, recommender systems (RS) have become
increasingly indispensable for mitigating information overload. Despite remarkable …

被引用次数：56 相关文章所有 3 个版本

[PDF] acm.org

Coral: collaborative retrieval-augmented large language models improve long-tail recommendation

J Wu, CC Chang, T Yu, Z He, J Wang, Y Hou… - Proceedings of the 30th …, 2024 - dl.acm.org

The long-tail recommendation is a challenging task for traditional recommender systems,
due to data sparsity and data imbalance issues. The recent development of large language …

被引用次数：6 相关文章所有 3 个版本

[PDF] arxiv.org

Foundation models for recommender systems: A survey and new perspectives

C Huang, T Yu, K Xie, S Zhang, L Yao… - arXiv preprint arXiv …, 2024 - arxiv.org

Recently, Foundation Models (FMs), with their extensive knowledge bases and complex
architectures, have offered unique opportunities within the realm of recommender systems …

被引用次数：15 相关文章所有 2 个版本

Enhancing health assessments with large language models: A methodological approach

X Wang, Y Zhou, G Zhou - Applied Psychology: Health and …, 2024 - Wiley Online Library

Health assessments have long been a significant research topic within the field of health
psychology. By analyzing the results of subject scales, these assessments effectively …

RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems

J Lian, Y Lei, X Huang, J Yao, W Xu, X Xie - Companion Proceedings of …, 2024 - dl.acm.org

This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize
recommender systems with the advanced capabilities of Large Language Models (LLMs) …

被引用次数：5 相关文章所有 3 个版本

[PDF] aclanthology.org

PepRec: Progressive Enhancement of Prompting for Recommendation

Y Yu, S Qi, B Li, D Niu - Proceedings of the 2024 Conference on …, 2024 - aclanthology.org

With large language models (LLMs) achieving remarkable breakthroughs in natural
language processing (NLP) domains, recent researchers have actively explored the …

Improving Expert Radiology Report Summarization by Prompting Large Language Models with a Layperson Summary

X Zhao, T Wang, A Rios - arXiv preprint arXiv:2406.14500, 2024 - arxiv.org

Radiology report summarization (RRS) is crucial for patient care, requiring concise"
Impressions" from detailed" Findings." This paper introduces a novel prompting strategy to …