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20202026
most citedA Survey of Large Language Models

1.5k citations · 2.3k across the 39 of their papers we have counts for

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Showing 2023Show all

8 papers · 1 filter

cs.IR2023

Scaling Law of Large Sequential Recommendation Models

Gaowei Zhang, Yupeng Hou, Hongyu Lu +3

Scaling of neural networks has recently shown great potential to improve the model capacity in various fields. Specifically, model performance has a power-law relationship with mod…

cs.IR2023★ 6 cited

Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation

Bowen Zheng, Yupeng Hou, Hongyu Lu +4

Recently, large language models (LLMs) have shown great potential in recommender systems, either improving existing recommendation models or serving as the backbone. However, there…

cs.IR2023★ 2 cited

AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems

Junjie Zhang, Yupeng Hou, Ruobing Xie +5

Recently, there has been an emergence of employing LLM-powered agents as believable human proxies, based on their remarkable decision-making capability. However, existing studies m…

cs.IR2023

Reciprocal Sequential Recommendation

Bowen Zheng, Yupeng Hou, Wayne Xin Zhao +2

Reciprocal recommender system (RRS), considering a two-way matching between two parties, has been widely applied in online platforms like online dating and recruitment. Existing RR…

cs.IR2023★ 25 cited

Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach

Junjie Zhang, Ruobing Xie, Yupeng Hou +3

In the past decades, recommender systems have attracted much attention in both research and industry communities, and a large number of studies have been devoted to developing effe…

cs.IR2023★ 23 cited

Large Language Models are Zero-Shot Rankers for Recommender Systems

Yupeng Hou, Junjie Zhang, Zihan Lin +4

Recently, large language models (LLMs) (e.g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks.…

cs.IR2023

Multi-grained Hypergraph Interest Modeling for Conversational Recommendation

Chenzhan Shang, Yupeng Hou, Wayne Xin Zhao +2

Conversational recommender system (CRS) interacts with users through multi-turn dialogues in natural language, which aims to provide high-quality recommendations for user's instant…

cs.CL2023★ 1.5k cited

A Survey of Large Language Models

Wayne Xin Zhao, Kun Zhou, Junyi Li +19

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 compre…