Information Retrieval - 31st China Conference, CCIR 2025, Shihezi, China, August 15–17, 2025, Revised Selected Papers

List of contents

.- CFC-CPI:Cross-scale Feature Fusion for Compound-Protein Interaction Prediction.
.- Reinforcement Learning-Based Attribute Alignment for Role-Playing of LLM.
.- Medical Question-physician Robustness Routing for Community Healthcare Services.
.- A Comparative Study of Specialized LLMs as Dense Retrievers.
.- Generalizable and Robust Phenotypic Drug Discovery.
.- FRAUDLLM: Zero-Shot Fraud Detection with Large Language Models.
.- FADE: Progressive Unlearning for Language Models via Adversarial Disruption and Editing.
.- MMKRF: A Domain-Specific Knowledge Retrieval Framework for RAG Systems in Materials Mechanics.
.- Multi-round Dialogue Embedding Based on Dynamic Context Awareness.

Summary

This book constitutes the refereed proceedings of the 31st China Conference on Information Retrieval, CCIR 2025, held in Wuhan, China, during August 15–17, 2025.
The 9 full papers were presented in this volume were carefully reviewed and selected from 13 submissions. This conference focuses on the deep integration of large language models with classic retrieval paradigms.

Product details

• Assisted by: Zhumin Chen (Editor), Yanyan Lan (Editor), Zhigang Li (Editor)
• Publisher: Springer EN
• Languages: English
• Product format: Paperback / Softback
• Release: 15.01.2026
• EAN: 9789819556366
• ISBN: 978-981-9556-36-6
• No. of pages: 140
• Illustrations: X, 140 p.
• Series: Lecture Notes in Computer Science
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; Künstliche Intelligenz, Data Mining, Artificial Intelligence, angewandte informatik, Data Warehousing, Wissensbasierte Systeme, Expertensysteme, Information Retrieval, Reinforcement Learning, Computer and Information Systems Applications, Information systems, Data Mining and Knowledge Discovery, Search Engines, Information Storage and Retrieval, Natural Language Processing (NLP), Question Answering, Generative AI, recommender systems, autonomous agents, Large Language Models (LLMs), Language Models, retrieval models and ranking, similarity measures, top-k retrieval in databases, Retrieval-Augmented Generation (RAG), Multimodal Retrieval, Novelty in information retrieval, Learning to rank, Knowledge-Enhanced Retrieval