Del 15418 - Lecture Notes in Computer Science

Information Retrieval

30th China Conference, CCIR 2024, Wuhan, China, October 18–20, 2024, Revised Selected Papers

Häftad, Engelska, 2025

Av Xiangnan He, Zhaochun Ren, Ruiming Tang

739 kr

Beställningsvara. Skickas inom 10-15 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

This book constitutes the refereed proceedings of the 30th China Conference on Information Retrieval, CCIR 2024, held in Wuhan, China, during October 18–20, 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 China’s internet industry and provides a broad platform for the exchange of the latest academic and technological achievements in the field of information retrieval.

Produktinformation

Utgivningsdatum2025-02-01
Mått155 x 235 x undefined mm
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor149
FörlagSpringer Nature Switzerland AG
ISBN9789819617098

Tillhör följande kategorier

Tillämpad datateknik inom Data och it
Systemvetenskap och AI inom Data och it
Artificiell intelligens inom Data och it
Databaser inom Data och it

.- 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.