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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

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

  1. C-Pack: Packed Resources For General Chinese EmbeddingsShitao Xiao, Zheng Liu, Peitian Zhang et al.485 citations · our summary
  2. The Power of Noise: Redefining Retrieval for RAG Systems ↗Florin Cuconasu, Giovanni Trappolini, Federico Siciliano et al.215 citations
  3. GraphGPT: Graph Instruction Tuning for Large Language Models ↗Jiabin Tang, Yuhao Yang, Wei Wei et al.199 citations
  4. Large Language Models can Accurately Predict Searcher Preferences ↗Paul Thomas, Seth E. Spielman, Nick Craswell et al.168 citations
  5. Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering ↗Zhentao Xu, Missy Meine C. Dela Cruz, Matthew Guevara et al.160 citations
  6. Data-efficient Fine-tuning for LLM-based Recommendation ↗Xinyu Lin, Wenjie Wang, Yongqi Li et al.157 citations
  7. LLaRA: Large Language-Recommendation Assistant ↗Jiayi Liao, Sihang Li, Zhengyi Yang et al.128 citations
  8. Fine-Tuning LLaMA for Multi-Stage Text RetrievalXueguang Ma, Liang Wang, Nan Yang et al.120 citations · our summary
  9. On Generative Agents in Recommendation ↗An Zhang, Yuxin Chen, Leheng Sheng et al.113 citations

All accepted papers (389)

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

  1. C-Pack: Packed Resources For General Chinese EmbeddingsShitao Xiao, Zheng Liu, Peitian Zhang et al.485 citations · our summary
  2. The Power of Noise: Redefining Retrieval for RAG Systems ↗Florin Cuconasu, Giovanni Trappolini, Federico Siciliano et al.215 citations
  3. GraphGPT: Graph Instruction Tuning for Large Language Models ↗Jiabin Tang, Yuhao Yang, Wei Wei et al.199 citations
  4. Large Language Models can Accurately Predict Searcher Preferences ↗Paul Thomas, Seth E. Spielman, Nick Craswell et al.168 citations
  5. Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering ↗Zhentao Xu, Missy Meine C. Dela Cruz, Matthew Guevara et al.160 citations
  6. Data-efficient Fine-tuning for LLM-based Recommendation ↗Xinyu Lin, Wenjie Wang, Yongqi Li et al.157 citations
  7. LLaRA: Large Language-Recommendation Assistant ↗Jiayi Liao, Sihang Li, Zhengyi Yang et al.128 citations
  8. Fine-Tuning LLaMA for Multi-Stage Text RetrievalXueguang Ma, Liang Wang, Nan Yang et al.120 citations · our summary
  9. On Generative Agents in Recommendation ↗An Zhang, Yuxin Chen, Leheng Sheng et al.113 citations
  10. Denoising Diffusion Recommender Model ↗Jujia Zhao, Wenjie Wang, Yiyan Xu et al.88 citations
  11. Large Language Models for Next Point-of-Interest Recommendation ↗Peibo Li, Maarten de Rijke, Hao Xue et al.88 citations
  12. Disentangled Contrastive Hypergraph Learning for Next POI Recommendation ↗Yantong Lai, Yijun Su, Lingwei Wei et al.55 citations
  13. MACRec: A Multi-Agent Collaboration Framework for Recommendation ↗Zhefan Wang, Yuanqing Yu, Wendi Zheng et al.54 citations
  14. 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)

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

 Top topics

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

Question answering (1)Semitic languages (1)Information retrieval & search (1)

 Fields

Arts and Humanities (1)Computer Science (1)

 Top authors

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

 Top institutions

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

works and citations from OpenAlex and our library.

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