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
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Abstract	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. The performance of learned ranking models may be largely affected by the usefulness of document features. To generate high-quality document ranking features, we capture the local context information of individual queries from the top-ranked documents of an initial retrieval using pseudo-relevance feedback. Based on the top-ranked feedback documents, we propose an attentive semi-supervised autoencoder to refine the ranked results using an optimized ranking-oriented reconstruction loss. Furthermore, we devise the hybrid listwise query constraints to capture the characteristics of relevant documents for different queries. We evaluate the proposed ranking model on LETOR collections including OHSUMED, MQ2007 and MQ2008. Our model produces better experimental results and consistent improvements of ranking performance over baseline methods.
AbstractList	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. The performance of learned ranking models may be largely affected by the usefulness of document features. To generate high-quality document ranking features, we capture the local context information of individual queries from the top-ranked documents of an initial retrieval using pseudo-relevance feedback. Based on the top-ranked feedback documents, we propose an attentive semi-supervised autoencoder to refine the ranked results using an optimized ranking-oriented reconstruction loss. Furthermore, we devise the hybrid listwise query constraints to capture the characteristics of relevant documents for different queries. We evaluate the proposed ranking model on LETOR collections including OHSUMED, MQ2007 and MQ2008. Our model produces better experimental results and consistent improvements of ranking performance over baseline methods.
Author	Xu, Kan Xu, Bo Lin, Yuan Lin, Hongfei
Author_xml	– sequence: 1 givenname: Bo orcidid: 0000-0001-5453-978X surname: Xu fullname: Xu, Bo email: xubo@dlut.edu.cn organization: School of Computer Science and Technology, Dalian University of Technology – sequence: 2 givenname: Hongfei surname: Lin fullname: Lin, Hongfei organization: School of Computer Science and Technology, Dalian University of Technology – sequence: 3 givenname: Yuan surname: Lin fullname: Lin, Yuan organization: WISE Lab, Faculty of Humanities and Social Sciences, Dalian University of Technology – sequence: 4 givenname: Kan surname: Xu fullname: Xu, Kan organization: School of Computer Science and Technology, Dalian University of Technology
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Cites_doi	10.1145/3130348.3130376 10.1145/3404835.3462886 10.1145/3404835.3462901 10.1145/3308560.3316736 10.1145/3459637.3482243 10.1145/2600428.2609542 10.1145/2939672.2939677 10.1145/2766462.2767753 10.1145/3331184.3331246 10.1145/3209978.3209981 10.1145/2835776.2835804 10.1145/3459637.3482179 10.1609/aaai.v29i1.9548 10.1145/3038912.3052579 10.1145/2740908.2742726 10.1145/3459637.3481899 10.1145/3209978.3210045 10.1016/j.neucom.2019.03.068 10.1145/3018661.3018699 10.1145/3209978.3209979 10.1214/aos/1013203451 10.3115/v1/P15-1107 10.1145/1102351.1102363 10.1145/3209978.3209985 10.1145/3404835.3463076 10.1561/1500000016 10.1145/3209978.3210048 10.1109/ACCESS.2022.3154767 10.1145/3209978.3209993 10.1145/1273496.1273513 10.1145/3459637.3482020 10.1007/s10791-009-9123-y 10.1016/j.ipm.2015.07.002 10.1145/3132847.3133049 10.1145/3331184.3331189 10.1145/2505515.2505665 10.1145/3404835.3463098 10.1609/aaai.v30i1.10159 10.1145/3209978.3209980 10.1145/3018661.3018720 10.1145/3459637.3482093 10.1145/3404835.3463088 10.18653/v1/N18-1142 10.1145/984321.984322 10.1145/3331184.3331316 10.1145/3459637.3482101 10.1561/2200000006 10.1145/3077136.3080786 10.1145/2983323.2983769 10.1109/ACCESS.2021.3124931 10.1145/3331184.3331296 10.1002/asi.4630270302
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Copyright_xml	– notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Snippet	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...
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Title	Context-aware ranking refinement with attentive semi-supervised autoencoders
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