Context-aware ranking refinement with attentive semi-supervised autoencoders

Learning to rank methods aim to learn a refined ranking model from labeled data for desired ranking performance. However, the learned model may not improve the performance on each individual query because the distributions of relevant documents among queries are diversified in document feature space...

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Veröffentlicht in:	Soft computing (Berlin, Germany) Jg. 26; H. 24; S. 13941 - 13952
Hauptverfasser:	Xu, Bo, Lin, Hongfei, Lin, Yuan, Xu, Kan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2022
Schlagworte:	Application of Soft Computing Artificial Intelligence Computational Intelligence Control Engineering Mathematical Logic and Foundations Mechatronics Robotics Pseudo-relevance feedback Information retrieval Learning to rank Ranking refinement Machine learning
ISSN:	1432-7643, 1433-7479
Online-Zugang:	Volltext
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