---
title: "KDD 2024 — accepted papers — Elamur Research"
description: "KDD 2024 — accepted papers: 1 paper in the Elamur Research library, with our summaries, ranked by citations."
canonical: "https://research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/2024/"
---
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# KDD 2024 — accepted papers

KDD 2024 is the 2024 edition of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). OpenAlex lists 642 papers of it; our library holds 1 with our summaries. Below: the most cited papers, every accepted paper, and ours with their topics, authors and institutions.

[**642** papers in the proceedings](#all) · [**1** work in our library](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/2024/#works) · [**1,006** citations of these works](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/2024/#works)

## Most cited papers

By their OpenAlex citations; a paper with our summary links to its page here, the others to the original.

1. **[A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/)**Wenqi Fan, Yujuan Ding, Liangbo Ning et al.*795 citations · our summary*
2. **[Foundation Models for Time Series Analysis: A Tutorial and Survey ↗](https://doi.org/10.1145/3637528.3671451)**Yuxuan Liang, Haomin Wen, Yuqi Nie et al.*225 citations*
3. **[UrbanGPT: Spatio-Temporal Large Language Models ↗](https://doi.org/10.1145/3637528.3671578)**Zhonghang Li, Lianghao Xia, Jiabin Tang et al.*173 citations*
4. **[FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning ↗](https://doi.org/10.1145/3637528.3671573)**Weirui Kuang, Bingchen Qian, Zitao Li et al.*137 citations*
5. **[Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System ↗](https://doi.org/10.1145/3637528.3671931)**Sein Kim, Hongseok Kang, Seungyoon Choi et al.*133 citations*
6. **[Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era ↗](https://doi.org/10.1145/3637528.3671458)**Sunhao Dai, Xu Chen, Shicheng Xu et al.*129 citations*
7. **[OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning ↗](https://doi.org/10.1145/3637528.3671582)**Rui Ye, Wenhao Wang, Jingyi Chai et al.*127 citations*
8. **[A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys) ↗](https://doi.org/10.1145/3637528.3671474)**Yashar Deldjoo, Zhankui He, Julian McAuley et al.*118 citations*
9. **[Fredformer: Frequency Debiased Transformer for Time Series Forecasting ↗](https://doi.org/10.1145/3637528.3671928)**Xihao Piao, Zheng Chen, Taichi Murayama et al.*114 citations*
10. **[UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction ↗](https://doi.org/10.1145/3637528.3671662)**Yuan Yuan, Jingtao Ding, Jie Feng et al.*112 citations*

## All accepted papers (642)

The most cited first; 1 with our summary link to their page here, the others to the original.

1. **[A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/)**Wenqi Fan, Yujuan Ding, Liangbo Ning et al.*795 citations · our summary*
2. **[Foundation Models for Time Series Analysis: A Tutorial and Survey ↗](https://doi.org/10.1145/3637528.3671451)**Yuxuan Liang, Haomin Wen, Yuqi Nie et al.*225 citations*
3. **[UrbanGPT: Spatio-Temporal Large Language Models ↗](https://doi.org/10.1145/3637528.3671578)**Zhonghang Li, Lianghao Xia, Jiabin Tang et al.*173 citations*
4. **[FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning ↗](https://doi.org/10.1145/3637528.3671573)**Weirui Kuang, Bingchen Qian, Zitao Li et al.*137 citations*
5. **[Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System ↗](https://doi.org/10.1145/3637528.3671931)**Sein Kim, Hongseok Kang, Seungyoon Choi et al.*133 citations*
6. **[Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era ↗](https://doi.org/10.1145/3637528.3671458)**Sunhao Dai, Xu Chen, Shicheng Xu et al.*129 citations*
7. **[OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning ↗](https://doi.org/10.1145/3637528.3671582)**Rui Ye, Wenhao Wang, Jingyi Chai et al.*127 citations*
8. **[A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys) ↗](https://doi.org/10.1145/3637528.3671474)**Yashar Deldjoo, Zhankui He, Julian McAuley et al.*118 citations*
9. **[Fredformer: Frequency Debiased Transformer for Time Series Forecasting ↗](https://doi.org/10.1145/3637528.3671928)**Xihao Piao, Zheng Chen, Taichi Murayama et al.*114 citations*
10. **[UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction ↗](https://doi.org/10.1145/3637528.3671662)**Yuan Yuan, Jingtao Ding, Jie Feng et al.*112 citations*
11. **[ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation ↗](https://doi.org/10.1145/3637528.3671751)**Tong Nie, Guoyang Qin, Wei Ma et al.*101 citations*
12. **[Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks ↗](https://doi.org/10.1145/3637528.3671977)**Jiaying Wu, Jiafeng Guo, Bryan Hooi*97 citations*
13. **[Graph Mamba: Towards Learning on Graphs with State Space Models ↗](https://doi.org/10.1145/3637528.3672044)**Ali Behrouz, Farnoosh Hashemi*80 citations*
14. **[FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model ↗](https://doi.org/10.1145/3637528.3671897)**Feijie Wu, Zitao Li, Yaliang Li et al.*78 citations*
15. **[AI for Education (AI4EDU): Advancing Personalized Education with LLM and Adaptive Learning ↗](https://doi.org/10.1145/3637528.3671498)**Qingsong Wen, Joleen Liang, Carles Sierra et al.*77 citations*
16. **[EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis ↗](https://doi.org/10.1145/3637528.3671552)**Zhiwei Liu, Kailai Yang, Qianqian Xie et al.*76 citations*
17. **[A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist ↗](https://doi.org/10.1145/3637528.3671801)**Wentao Zhang, Lingxuan Zhao, Haochong Xia et al.*73 citations*
18. **[A Survey of Large Language Models for Graphs ↗](https://doi.org/10.1145/3637528.3671460)**Xubin Ren, Jiabin Tang, Dawei Yin et al.*72 citations*
19. **[TnT-LLM: Text Mining at Scale with Large Language Models ↗](https://doi.org/10.1145/3637528.3671647)**Mengting Wan, Tara Safavi, Sujay Kumar Jauhar et al.*71 citations*
20. **[Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting ↗](https://doi.org/10.1145/3637528.3671961)**Zheng Dong, Renhe Jiang, Haotian Gao et al.*68 citations*
21. **[A Review of Graph Neural Networks in Epidemic Modeling ↗](https://doi.org/10.1145/3637528.3671455)**Zewen Liu, Guancheng Wan, B. Aditya Prakash et al.*67 citations*
22. **[A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide ↗](https://doi.org/10.1145/3637528.3671457)**Sunwoo Kim, Soo Yong Lee, Yue Gao et al.*60 citations*
23. **[FNSPID: A Comprehensive Financial News Dataset in Time Series ↗](https://doi.org/10.1145/3637528.3671629)**Zihan Dong, Fan Xinyu, Zhiyuan Peng*60 citations*
24. **[Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey ↗](https://doi.org/10.1145/3637528.3671473)**Qijiong Liu, Jieming Zhu, Yanting Yang et al.*60 citations*
25. **[LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models ↗](https://doi.org/10.1145/3637528.3671810)**Aoxiao Zhong, Dengyao Mo, Guiyang Liu et al.*59 citations*

[See all 642 papers in KDD 2024 — accepted papers →](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/2024/papers/)

## Papers with our summary in our library (1)

The most cited first (OpenAlex citations plus our library's).

1. **[A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/) [ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/)**Wenqi Fan; Yujuan Ding; Liangbo Ning; Shijie Wang; Hengyun Li; Dawei Yin; Tat-Seng Chua;, Qing Li*2024 · 1,006 citations*

## Top topics

Topics of its works in our library, with the number of works on each.

[Language models & pretraining (1)](https://md.research.elamur.ai/free/en/topics/language-models/) · [Information retrieval & search (1)](https://md.research.elamur.ai/free/en/topics/information-retrieval/)

## Fields

[Arts and Humanities (1)](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science (1)](https://md.research.elamur.ai/free/en/shelves/computer-science/)

## Top authors

Authors with a page who published here most often: their works here, and the citations of those works.

[🧑‍🔬**Qing Li**1 work here · 1,006 citations](https://md.research.elamur.ai/free/en/authors/qing-li/)

## Top institutions

Institutions on the bylines of its works here, by the number of works.

[🎓**Hong Kong Polytechnic University** Hong Kong · China 1 work here](https://md.research.elamur.ai/free/en/universities/hong-kong-polytechnic-university/) · [🎓**National University of Singapore** Singapore · Singapore 1 work here](https://md.research.elamur.ai/free/en/universities/national-university-of-singapore/) · [🏢**Baidu** Beijing · China 1 work here](https://md.research.elamur.ai/free/en/companies/baidu/)

works and citations from OpenAlex and our library.

- [← previous KDD 2023](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/2023/)
- [↑ back to KDD](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/)