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Information Retrieval: 30th China Conference, CCIR 2024, Wuhan, China, October 1820, 2024, Revised Selected Papers [Pehme köide]

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  • Formaat: Paperback / softback, 149 pages, kõrgus x laius: 235x155 mm, 31 Illustrations, color; 1 Illustrations, black and white; X, 149 p. 32 illus., 31 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 15418
  • Ilmumisaeg: 01-Feb-2025
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 981961709X
  • ISBN-13: 9789819617098
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  • Formaat: Paperback / softback, 149 pages, kõrgus x laius: 235x155 mm, 31 Illustrations, color; 1 Illustrations, black and white; X, 149 p. 32 illus., 31 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 15418
  • Ilmumisaeg: 01-Feb-2025
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 981961709X
  • ISBN-13: 9789819617098
This book constitutes the refereed proceedings of the 30th China Conference on Information Retrieval, CCIR 2024, held in Wuhan, China, during October 1820, 2024.





The 11 full papers presented in this volume were carefully reviewed and selected from 26 submissions. As the flagship conference of CIPS, CCIR focuses on the development of Chinas internet industry and provides a broad platform for the exchange of the latest academic and technological achievements in the field of information retrieval.

.- Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models.
.- A Dual-Aligned Model for Multimodal Recommendation.
.- CASINet: A Context-Aware Social Interaction Rumor Detection Network.
.- A Claim Decomposition Benchmark for Long-form Answer Verification.
.- Dual-granularity Hierarchical Fusion Network for Multimodal Humor Recognition on Memes.
.- Exploring the Potential of Dimension Reduction in Building Efficient Dense Retrieval Systems.
.- Relation Extraction Model Based on Overlap Rules and Abductive Learning.
.- Multi-task Instruction Tuning for Temporal Question Answering over Knowledge Graphs.
.- On the Capacity of Citation Generation by Large Language Models.
.- Are Large Language Models More Honest in Their Probabilistic or Verbalized Confidence?.
.- QUITO: Accelerating Long-Context Reasoning through Query-Guided Context Compression.