Information Retrieval : 30th China Conference, CCIR 2024, Wuhan, China, October 18–20, 2024, Revised Selected Papers / edited by Xiangnan He, Zhaochun Ren, Ruiming Tang.

Format:

Book

Contributor:

He, Xiangnan, Editor.

Ren, Zhaochun, Editor.

Tang, Ruiming., Editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 15418

Language:

English

Subjects (All):

Information storage and retrieval systems.

Application software.

Data mining.

Artificial intelligence.

Information Storage and Retrieval.

Computer and Information Systems Applications.

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Local Subjects:

Information Storage and Retrieval.

Computer and Information Systems Applications.

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Physical Description:

1 online resource (X, 149 p. 32 illus., 31 illus. in color.)

Edition:

1st ed. 2025.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2025.

Summary:

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.

Contents:

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9789819617104

9819617103

OCLC:

1496391747