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Learning to rank user intent

Published: 24 October 2011 Publication History

Abstract

Personalized retrieval models aim at capturing user interests to provide personalized results that are tailored to the respective information needs. User interests are however widely spread, subject to change, and cannot always be captured well, thus rendering the deployment of personalized models challenging. We take a different approach and study ranking models for user intent. We exploit user feedback in terms of click data to cluster ranking models for historic queries according to user behavior and intent. Each cluster is finally represented by a single ranking model that captures the contained search interests expressed by users. Once new queries are issued, these are mapped to the clustering and the retrieval process diversifies possible intents by combining relevant ranking functions. Empirical evidence shows that our approach significantly outperforms baseline approaches on a large corporate query log.

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  • (2024)Learning Multiple Multicriteria Additive Models from Heterogeneous PreferencesAlgorithmic Decision Theory10.1007/978-3-031-73903-3_14(207-224)Online publication date: 16-Oct-2024
  • (2021)Learning to Rank for Educational Search EnginesIEEE Transactions on Learning Technologies10.1109/TLT.2021.307519614:2(211-225)Online publication date: 1-Apr-2021
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cover image ACM Conferences
CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management
October 2011
2712 pages
ISBN:9781450307178
DOI:10.1145/2063576
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 24 October 2011

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Author Tags

  1. clickthrough data
  2. clustering
  3. ranking
  4. relevance judgement
  5. search behavior
  6. search engine
  7. training

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  • (2024)User versus institutional perspectives of metadata and searching: an investigation of online access to cultural heritage content during the COVID-19 pandemicInternational Journal on Digital Libraries10.1007/s00799-023-00385-y25:1(105-121)Online publication date: 1-Mar-2024
  • (2024)Learning Multiple Multicriteria Additive Models from Heterogeneous PreferencesAlgorithmic Decision Theory10.1007/978-3-031-73903-3_14(207-224)Online publication date: 16-Oct-2024
  • (2021)Learning to Rank for Educational Search EnginesIEEE Transactions on Learning Technologies10.1109/TLT.2021.307519614:2(211-225)Online publication date: 1-Apr-2021
  • (2021)Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challengesComplex & Intelligent Systems10.1007/s40747-021-00342-99:3(2773-2799)Online publication date: 5-Apr-2021
  • (2017)Clustered Model Adaption for Personalized Sentiment AnalysisProceedings of the 26th International Conference on World Wide Web10.1145/3038912.3052693(937-946)Online publication date: 3-Apr-2017
  • (2017)Robust Learning to Rank Based on Portfolio Theory and AMOSA AlgorithmIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2016.258478647:6(1007-1018)Online publication date: Jun-2017
  • (2017)Intent-Aware Contextual Recommendation System2017 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2017.8(1-8)Online publication date: Nov-2017
  • (2016)A Risk Calculus Extension to the XACML LanguageProceedings of the XII Brazilian Symposium on Information Systems on Brazilian Symposium on Information Systems: Information Systems in the Cloud Computing Era - Volume 110.5555/3021955.3022010(321-328)Online publication date: 17-May-2016
  • (2016)Click-based Hot Fixes for Underperforming Torso QueriesProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2911500(195-204)Online publication date: 7-Jul-2016
  • (2015)A Novel Crossing Minimization Ranking MethodApplied Artificial Intelligence10.1080/08839514.2015.98301429:1(66-99)Online publication date: 6-Jan-2015
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