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Context-sensitive ranking

Published: 27 June 2006 Publication History

Abstract

Contextual preferences take the form that item i1 is preferred to item i2 in the context of X. For example, a preference might state the choice for Nicole Kidman over Penelope Cruz in drama movies, whereas another preference might choose Penelope Cruz over Nicole Kidman in the context of Spanish dramas. Various sources provide preferences independently and thus preferences may contain cycles and contradictions. We reconcile democratically the preferences accumulated from various sources and use them to create a priori orderings of tuples in an off-line preprocessing step. Only a few representative orders are saved, each corre-sponding to a set of contexts. These orders and associated contexts are used at query time to expeditiously provide ranked answers. We formally define contextual preferences, provide algorithms for creating orders and processing queries, and present experimental results that show their efficacy and practical utility.

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    cover image ACM Conferences
    SIGMOD '06: Proceedings of the 2006 ACM SIGMOD international conference on Management of data
    June 2006
    830 pages
    ISBN:1595934340
    DOI:10.1145/1142473
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    Publication History

    Published: 27 June 2006

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    1. preferences
    2. ranking

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    • (2023)Query Relaxation for Uncertain Spatiotemporal XML DataUncertain Spatiotemporal Data Management for the Semantic Web10.4018/978-1-6684-9108-9.ch020(463-483)Online publication date: 15-Dec-2023
    • (2023)Personalized OLAP queries under Hierarchical Visualization Constraint*2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA)10.1109/INISTA59065.2023.10310545(1-6)Online publication date: 20-Sep-2023
    • (2023)A Personalized Multidimensional Navigation in a Limited Visualization Context2023 International Conference on Cyberworlds (CW)10.1109/CW58918.2023.00018(54-61)Online publication date: 3-Oct-2023
    • (2023)Towards intelligent database systems using clusters of SQL transactionsKnowledge and Information Systems10.1007/s10115-023-01850-565:7(2863-2894)Online publication date: 16-Mar-2023
    • (2022)Query Relaxation and Result Ranking for Uncertain Spatiotemporal XML DataJournal of Database Management10.4018/JDM.31397033:1(1-19)Online publication date: 1-Jan-2022
    • (2022)Preference-based inconsistency-tolerant query answering under existential rulesArtificial Intelligence10.1016/j.artint.2022.103772312(103772)Online publication date: Nov-2022
    • (2021)Ontology Evolution Using Recoverable SQL LogsService-Oriented Computing – ICSOC 2020 Workshops10.1007/978-3-030-76352-7_46(509-517)Online publication date: 30-May-2021
    • (2021) Adaptive query relaxation and top‐ k result sorting of fuzzy spatiotemporal data based on XML International Journal of Intelligent Systems10.1002/int.22781Online publication date: 13-Dec-2021
    • (2020)Foundations of Context-aware Preference PropagationJournal of the ACM10.1145/337571367:1(1-43)Online publication date: 15-Jan-2020
    • (2020)Personalization in text information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2423471:3(349-369)Online publication date: 28-Jan-2020
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