| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNISA996696878303316 |
|
|
Autore |
Li Zhigang |
|
|
Titolo |
Information Retrieval : 31st China Conference, CCIR 2025, Shihezi, China, August 15–17, 2025, Revised Selected Papers / / edited by Zhigang Li, Yanyan Lan, Zhumin Chen |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2026 |
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[1st ed. 2026.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (223 pages) |
|
|
|
|
|
|
Collana |
|
Lecture Notes in Computer Science, , 1611-3349 ; ; 16369 |
|
|
|
|
|
|
Altri autori (Persone) |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
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 |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di contenuto |
|
-- 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. |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
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. |
|
|
|
|
|
| |