1.

Record Nr.

UNINA9910983342003321

Titolo

Advances on Graph-Based Approaches in Information Retrieval : First International Workshop, IRonGraphs 2024, Glasgow, UK, March 24, 2024, Proceedings / / edited by Ludovico Boratto, Daniele Malitesta, Mirko Marras, Giacomo Medda, Cataldo Musto, Erasmo Purificato

Pubbl/distr/stampa

Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025

ISBN

3-031-71382-6

Edizione

[1st ed. 2025.]

Descrizione fisica

1 online resource (98 pages)

Collana

Communications in Computer and Information Science, , 1865-0937 ; ; 2197

Disciplina

658.812

Soggetti

Natural language processing (Computer science)

Machine learning

Information storage and retrieval systems

Database management

Data mining

Natural Language Processing (NLP)

Machine Learning

Information Storage and Retrieval

Database Management System

Data Mining and Knowledge Discovery

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

.-Soccer-GraphRAG: Applications of GraphRAG in Soccer.  -- Enhanced Semantic Understanding with Graph-based Information Retrieval.  -- Identifying Shopping Intent in Product QA for Proactive Recommendations.  -- KGUF: Simple Knowledge-aware Graph-based Recommender with User-based Semantic Features Filtering.  -- The Effectiveness of Graph Contrastive Learning on Mathematical Information Retrieval.  -- The Impact of Source-Target Node Distance on Vicious Adversarial Attacks in Social Network Recommendation Systems.

Sommario/riassunto

This book constitutes the refereed proceedings of the First



International Workshop on Graph-Based Approaches in Information Retrieval, IRonGraphs 2024, held in Glasgow, UK, on March 24, 2024. The 6 full papers included in this book were carefully reviewed and selected from 14 submissions. They focus on diverse novel contributions, with presentations on knowledge-aware graph-based recommender systems using user-based semantic features filtering, source-target node distance impacts on adversarial attacks in social network recommendations.