Recommender systems in the era of large language models (llms)

Z Zhao, W Fan, J Li, Y Liu, X Mei, Y Wang… - arxiv preprint arxiv …, 2023 - arxiv.org
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an important component of our daily life, providing personalized suggestions …

When large language models meet personalization: Perspectives of challenges and opportunities

J Chen, Z Liu, X Huang, C Wu, Q Liu, G Jiang, Y Pu… - World Wide Web, 2024 - Springer
The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …

A survey of large language models

WX Zhao, K Zhou, J Li, T Tang, X Wang, Y Hou… - arxiv preprint arxiv …, 2023 - arxiv.org
Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

Wizardlm: Empowering large language models to follow complex instructions

C Xu, Q Sun, K Zheng, X Geng, P Zhao, J Feng… - arxiv preprint arxiv …, 2023 - arxiv.org
Training large language models (LLMs) with open-domain instruction following data brings
colossal success. However, manually creating such instruction data is very time-consuming …

A survey on large language models for recommendation

L Wu, Z Zheng, Z Qiu, H Wang, H Gu, T Shen, C Qin… - World Wide Web, 2024 - Springer
Abstract Large Language Models (LLMs) have emerged as powerful tools in the field of
Natural Language Processing (NLP) and have recently gained significant attention in the …

Large language models are zero-shot rankers for recommender systems

Y Hou, J Zhang, Z Lin, H Lu, R **e, J McAuley… - … on Information Retrieval, 2024 - Springer
Recently, large language models (LLMs)(eg, GPT-4) have demonstrated impressive general-
purpose task-solving abilities, including the potential to approach recommendation tasks …

Recommendation as instruction following: A large language model empowered recommendation approach

J Zhang, R **e, Y Hou, X Zhao, L Lin… - ACM Transactions on …, 2023 - dl.acm.org
In the past decades, recommender systems have attracted much attention in both research
and industry communities. Existing recommendation models mainly learn the underlying …

Towards open-world recommendation with knowledge augmentation from large language models

Y **, W Liu, J Lin, X Cai, H Zhu, J Zhu, B Chen… - Proceedings of the 18th …, 2024 - dl.acm.org
Recommender system plays a vital role in various online services. However, its insulated
nature of training and deploying separately within a specific closed domain limits its access …

Is chatgpt fair for recommendation? evaluating fairness in large language model recommendation

J Zhang, K Bao, Y Zhang, W Wang, F Feng… - Proceedings of the 17th …, 2023 - dl.acm.org
The remarkable achievements of Large Language Models (LLMs) have led to the
emergence of a novel recommendation paradigm—Recommendation via LLM (RecLLM) …

Data-efficient Fine-tuning for LLM-based Recommendation

X Lin, W Wang, Y Li, S Yang, F Feng, Y Wei… - Proceedings of the 47th …, 2024 - dl.acm.org
Leveraging Large Language Models (LLMs) for recommendation has recently garnered
considerable attention, where fine-tuning plays a key role in LLMs' adaptation. However, the …