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ECIR2026
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Enhancing Job Search Effectiveness with LLM-Powered Context-Aware Query Reformulation
This abstract has open access
Abstract Summary
Abstract ID :
NKDR156
Submission Type
Demos
Submission Topics
Applications Machine Learning and Large Language Models Search and ranking System aspects
Associated Sessions
Poster Session (Demos)
Author
Co-Authors
QV
Quang Hieu Vu
Upwork
BN
Behnaz Nojavanasghari
Senior Machine Learning Manager
,
Upwork
FY
Frank Yang
Upwork
AR
Andrew Rabinovich
Upwork
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Abstract ID
Abstract Title
Abstract Topic
Submission Type
Primary Author
NKDR143
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Applications Machine Learning and Large Language Models Recommender systems Search and ranking
Demos
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NKDR166
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Applications Machine Learning and Large Language Models Search and ranking Societally-motivated IR research
Demos
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NKDR168
Context Engineering for Agentic Data Science
Demos
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NKDR169
Creating Specialized RAG-Based Search Engines Using the Open Web Index
Demos
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NKDR209
GutBrainKB: Exploring the Gut¬CBrain Interaction through aReliable Biomedical KB
Demos
Ornella Irrera
NKDR159
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Applications Machine Learning and Large Language Models Search and ranking
Demos
Rodrigo Duarte
NKDR160
LectureChat: Hybrid RAG over Wikipedia and Multilingual Lectures
Applications Conversational search and recommender systems Societally-motivated IR research
Demos
Markos Dimitsas
NKDR163
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Demos
Gregor Donabauer
NKDR27
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Evaluation researchMachine Learning and Large Language ModelsRecommender systems
Demos
Lukas Wegmeth
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