Advances on Graph-Based Approaches in Information Retrieval: First International Workshop, IRonGraphs...

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Ludovico Boratto Daniele Malitesta

Advances on Graph-Based Approaches in Information Retrieval

First International Workshop, IRonGraphs 2024, Glasgow, UK, March 24, 2024, Proceedings

Ludovico Boratto, Daniele Malitesta, Mirko Marras , Giacomo Medda

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English

Springer International Publishing AG
10 October 2024

Information retrieval; Expert & knowledge-based systems; Natural language & machine translation; Machine learning

Series: Communications in Computer and Information Science

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.

Edited by: Ludovico Boratto, Daniele Malitesta, Mirko Marras, Giacomo Medda, Cataldo Musto
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Edition: 2025 ed.
Volume: 2197
Dimensions: Height: 235mm, Width: 155mm,
ISBN: 9783031713811
ISBN 10: 3031713818
Series: Communications in Computer and Information Science
Pages: 89
Publication Date: 10 October 2024
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

.-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.