Buch, Englisch, 456 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 721 g
47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6-10, 2025, Proceedings, Part II
Buch, Englisch, 456 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 721 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-031-88710-9
Verlag: Springer Nature Switzerland
The five-volume set LNCS 15572, 15573, 15574, 15575 and 15576 constitutes the refereed conference proceedings of the 47th European Conference on Information Retrieval, ECIR 2025, held in Lucca, Italy, during April 6–10, 2025.
The 52 full papers, 11 findings, 42 short papers and 76 papers of other types presented in these proceedings were carefully reviewed and selected from 530 submissions. The accepted papers cover the state-of-the-art in information retrieval and recommender systems: user aspects, system and foundational aspects, artificial intelligence and machine learning, applications, evaluation, new social and technical challenges, and other topics of direct or indirect relevance to search and recommendation.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Information Retrieval
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Warehouse
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
Weitere Infos & Material
.- Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders.
.- Patent Figure Classification using Large Vision-language Models.
.- E!cient Session Retrieval Using Topical Index Shards.
.- Feature Attribution Explanations of Session-based Recommendations.
.- Evaluating Sequential Recommendations in the Wild: A Case Study on offine Accuracy, Click Rates, and Consumption.
.- Graph-Convolutional Networks: Named Entity Recognition and Large Language Model Embedding in Document Clustering.
.- Exploring the relationship between listener receptivity and source of music recommendations.
.- News Without Borders: Domain Adaptation of Multilingual Sentence Embeddings for Cross-lingual News Recommendation.
.- Maybe you are looking for CroQS: Cross-modal Query Suggestion for Text-to-Image Retrieval.
.- Evaluating LLM Abilities to Understand Tabular Electronic Health Records: A Comprehensive Study of Patient Data Extraction and Retrieval.
.- MVAM: Multi-View Attention Method for Fine-grained Image-Text Matching.
.- An Investigation of Prompt Variations for Zero-shot LLM-based Rankers.
.- Query Performance Prediction using Dimension Importance Estimators.
.- Uncertainty Estimation in the Real World: A study on Music Emotion Recognition.
.- Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Models.
.- Semi-Supervised Image-Based Narrative Extraction: A Case Study with Historical Photographic Records.
.- LLM is Knowledge Graph Reasoner: LLM’s Intuition-aware Knowledge Graph Reasoning for Cold-start Sequential Recommendation.
.- PEIR: Modeling Performance in Neural Information Retrieval.
.- mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval.
.- Leveraging Retrieval-Augmented Generation for Keyphrase Synonym Suggestion.
.- Can Large Language Models E#ectively Rerank News Articles for Background Linking?.
.- OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System.
.- CUP: a Framework for Resource-E!cient Review-Based Recommenders.
.- Towards E!cient and Explainable Hate Speech Detection via Model Distillation.
.- Visual Latent Captioning - Towards Verbalizing Vision Transformer Encoders.
.- On the Robustness of Generative Information Retrieval Models: An Out-of-Distribution Perspective.
.- Towards Reliable Testing for Multiple Information Retrieval System Comparisons.
.- Leveraging High-Resolution Features for Improved Deep Hashing-based Image Retrieval.
