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Context-sensitive ranking for document retrieval

Authors:

Liang Jeff Chen,

Yannis PapakonstantinouAuthors Info & Claims

SIGMOD '11: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data

Pages 757 - 768

https://doi.org/10.1145/1989323.1989403

Published: 12 June 2011 Publication History

Abstract

We study the problem of context-sensitive ranking for document retrieval, where a context is defined as a sub-collection of documents, and is specified by queries provided by domain-interested users. The motivation of context-sensitive search is that the ranking of the same keyword query generally depends on the context. The reason is that the underlying keyword statistics differ significantly from one context to another. The query evaluation challenge is the computation of keyword statistics at runtime, which involves expensive online aggregations. We appropriately leverage and extend materialized view research in order to deliver algorithms and data structures that evaluate context-sensitive queries efficiently. Specifically, a number of views are selected and materialized, each corresponding to one or more large contexts. Materialized views are used at query time to compute statistics which are used to compute ranking scores. Experimental results show that the context-sensitive ranking generally improves the ranking quality, while our materialized view-based technique improves the query efficiency.

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Cited By

Dong WMao P(2021)Cluster Analysis of Influencing Factors of Regional Economic Growth Based on Random Walk Model2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA)10.1109/ICECA52323.2021.9675884(1243-1246)Online publication date: 2-Dec-2021
https://doi.org/10.1109/ICECA52323.2021.9675884
Meng XZhang XTang YBi C(2017)Adaptive query relaxation and top-k result ranking over autonomous web databasesKnowledge and Information Systems10.1007/s10115-016-0982-451:2(395-433)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s10115-016-0982-4
de Souza Inacio AAndrade Rvon Wangenheim Ade Macedo D(2014)Designing an information retrieval system for the STT/SC2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)10.1109/HealthCom.2014.7001893(500-505)Online publication date: Oct-2014
https://doi.org/10.1109/HealthCom.2014.7001893
Show More Cited By

Index Terms

Context-sensitive ranking for document retrieval
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database query processing
  2. Information retrieval
    1. Retrieval models and ranking
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

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Published In

cover image ACM Conferences

SIGMOD '11: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data

June 2011

1364 pages

ISBN:9781450306614

DOI:10.1145/1989323

General Chair:
Timos Sellis
IMIS/RC Athena
,
Program Chair:
Renée J. Miller
University of Toronto
,
Publications Chairs:
Anastasios Kementsietsidis
IBM T.J. Watson Research Center
,
Yannis Velegrakis
University of Trento

Copyright © 2011 ACM.

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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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2011

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SIGMOD/PODS '11

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SIGMOD

SIGMOD/PODS '11: International Conference on Management of Data

June 12 - 16, 2011

Athens, Greece

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Dong WMao P(2021)Cluster Analysis of Influencing Factors of Regional Economic Growth Based on Random Walk Model2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA)10.1109/ICECA52323.2021.9675884(1243-1246)Online publication date: 2-Dec-2021
https://doi.org/10.1109/ICECA52323.2021.9675884
Meng XZhang XTang YBi C(2017)Adaptive query relaxation and top-k result ranking over autonomous web databasesKnowledge and Information Systems10.1007/s10115-016-0982-451:2(395-433)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s10115-016-0982-4
de Souza Inacio AAndrade Rvon Wangenheim Ade Macedo D(2014)Designing an information retrieval system for the STT/SC2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)10.1109/HealthCom.2014.7001893(500-505)Online publication date: Oct-2014
https://doi.org/10.1109/HealthCom.2014.7001893
Köhncke BBalke W(2013)Context-Sensitive Ranking Using Cross-Domain Knowledge for Chemical Digital LibrariesResearch and Advanced Technology for Digital Libraries10.1007/978-3-642-40501-3_29(285-296)Online publication date: 2013
https://doi.org/10.1007/978-3-642-40501-3_29
Chen JChen TGuo HYu TWang WTian JChin AChen E(2012)Context-aware document recommendation by mining sequential access dataProceedings of the 1st International Workshop on Context Discovery and Data Mining10.1145/2346604.2346612(1-7)Online publication date: 12-Aug-2012
https://dl.acm.org/doi/10.1145/2346604.2346612

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