SIGIR 2024 — accepted papers
SIGIR 2024 is the 2024 edition of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). OpenAlex lists 389 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.
Most cited papers
- C-Pack: Packed Resources For General Chinese EmbeddingsShitao Xiao, Zheng Liu, Peitian Zhang et al.485 citations · our summary
- The Power of Noise: Redefining Retrieval for RAG Systems ↗Florin Cuconasu, Giovanni Trappolini, Federico Siciliano et al.215 citations
- GraphGPT: Graph Instruction Tuning for Large Language Models ↗Jiabin Tang, Yuhao Yang, Wei Wei et al.199 citations
- Large Language Models can Accurately Predict Searcher Preferences ↗Paul Thomas, Seth E. Spielman, Nick Craswell et al.168 citations
- Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering ↗Zhentao Xu, Missy Meine C. Dela Cruz, Matthew Guevara et al.160 citations
- Data-efficient Fine-tuning for LLM-based Recommendation ↗Xinyu Lin, Wenjie Wang, Yongqi Li et al.157 citations
- Evaluating Retrieval Quality in Retrieval-Augmented Generation ↗Alireza Salemi, Hamed Zamani155 citations
- LLaRA: Large Language-Recommendation Assistant ↗Jiayi Liao, Sihang Li, Zhengyi Yang et al.128 citations
- Fine-Tuning LLaMA for Multi-Stage Text RetrievalXueguang Ma, Liang Wang, Nan Yang et al.120 citations · our summary
- On Generative Agents in Recommendation ↗An Zhang, Yuxin Chen, Leheng Sheng et al.113 citations
All accepted papers (389)
- C-Pack: Packed Resources For General Chinese EmbeddingsShitao Xiao, Zheng Liu, Peitian Zhang et al.485 citations · our summary
- The Power of Noise: Redefining Retrieval for RAG Systems ↗Florin Cuconasu, Giovanni Trappolini, Federico Siciliano et al.215 citations
- GraphGPT: Graph Instruction Tuning for Large Language Models ↗Jiabin Tang, Yuhao Yang, Wei Wei et al.199 citations
- Large Language Models can Accurately Predict Searcher Preferences ↗Paul Thomas, Seth E. Spielman, Nick Craswell et al.168 citations
- Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering ↗Zhentao Xu, Missy Meine C. Dela Cruz, Matthew Guevara et al.160 citations
- Data-efficient Fine-tuning for LLM-based Recommendation ↗Xinyu Lin, Wenjie Wang, Yongqi Li et al.157 citations
- Evaluating Retrieval Quality in Retrieval-Augmented Generation ↗Alireza Salemi, Hamed Zamani155 citations
- LLaRA: Large Language-Recommendation Assistant ↗Jiayi Liao, Sihang Li, Zhengyi Yang et al.128 citations
- Fine-Tuning LLaMA for Multi-Stage Text RetrievalXueguang Ma, Liang Wang, Nan Yang et al.120 citations · our summary
- On Generative Agents in Recommendation ↗An Zhang, Yuxin Chen, Leheng Sheng et al.113 citations
- When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications ↗Qidong Liu, Xian Wei Wu, Xiangyu Zhao et al.93 citations
- Denoising Diffusion Recommender Model ↗Jujia Zhao, Wenjie Wang, Yiyan Xu et al.88 citations
- Large Language Models for Next Point-of-Interest Recommendation ↗Peibo Li, Maarten de Rijke, Hao Xue et al.88 citations
- TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems ↗Peiyan Zhang, Yuchen Yan, Xi Zhang et al.88 citations
- SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation ↗Yuxi Liu, Lianghao Xia, Chao Huang63 citations
- A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models ↗Shengyao Zhuang, Honglei Zhuang, Bevan Koopman et al.60 citations
- CMCLRec: Cross-modal Contrastive Learning for User Cold-start Sequential Recommendation ↗Xiaolong Xu, Hongsheng Dong, Lianyong Qi et al.57 citations
- Disentangled Contrastive Hypergraph Learning for Next POI Recommendation ↗Yantong Lai, Yijun Su, Lingwei Wei et al.55 citations
- LDRE: LLM-based Divergent Reasoning and Ensemble for Zero-Shot Composed Image Retrieval ↗Zhenyu Yang, Dizhan Xue, Shengsheng Qian et al.54 citations
- MACRec: A Multi-Agent Collaboration Framework for Recommendation ↗Zhefan Wang, Yuanqing Yu, Wendi Zheng et al.54 citations
- Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation ↗Alireza Salemi, Surya Teja Kallumadi, Hamed Zamani52 citations
- Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation ↗Mingshi Yan, Fan Liu, Jing Sun et al.51 citations
- LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction ↗Chenhao Fang, Xiaohan Li, Zezhong Fan et al.51 citations
- Large Language Models and Future of Information Retrieval: Opportunities and Challenges ↗ChengXiang Zhai49 citations
- Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations ↗Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli et al.48 citations
See all 389 papers in SIGIR 2024 — accepted papers →
Papers with our summary in our library (1)
- ArabicaQA: A Comprehensive Dataset for Arabic Question AnsweringInternational ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)Abdelrahman Abdallah, Mahmoud Kasem, Mahmoud Abdalla, et al.2024 · 47 citations
Top topics
Question answering (1)Semitic languages (1)Information retrieval & search (1)
Fields
Arts and Humanities (1)Computer Science (1)
Top authors
Top institutions
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