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A personalized result merging method for metasearch engine

Authors:

Boyu ZhouAuthors Info & Claims

ICSCA '17: Proceedings of the 6th International Conference on Software and Computer Applications

Pages 203 - 207

https://doi.org/10.1145/3056662.3056698

Published: 26 February 2017 Publication History

Abstract

Metasearch engine integrates the search results from multiple sources, and improves recall in the big data environment. Result merging is a key component which will greatly affect the effectiveness of a metasearch engine. Great progress has been made in this area, however few studies have focused on providing personalized results for different users. This paper proposes a personalized method for merging results of metasearch engine according to a variety of factors, including the user interest distribution, the total number of component search engines exploited, the number of the results that each engine returned, the ranking position of document in each search engine and the number of component search engines who returned the document. Compared with the Borda Fuse method and rCombMNZ method, experimental results show that the proposed model performs better on mean average precision, improving the significance of documents which seldom occurred but are important to the user. By considering the distribution of user interest, the method also has ability of providing personalized result for different users. It is feasible to provide the useful search results more effectively.

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Cited By

Kumar RBhardwaj RGupta SBalasubramani RVerma M(2025)Performance ranking of metasearch engines based on recall and re-ranking aggregations approachPerformance Measurement and Metrics10.1108/PMM-12-2024-0061Online publication date: 4-Mar-2025
https://doi.org/10.1108/PMM-12-2024-0061
Akritidis LFevgas ABozanis PManolopoulos Y(2022)An unsupervised distance-based model for weighted rank aggregation with list pruningExpert Systems with Applications10.1016/j.eswa.2022.117435202(117435)Online publication date: Sep-2022
https://doi.org/10.1016/j.eswa.2022.117435
Wang MLi QLin Y(2020)An Organizational Structure and Self-adaptive Mechanism for Holonic Multi-Agent SystemsIEEE Access10.1109/ACCESS.2020.3014694(1-1)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3014694
Show More Cited By

Index Terms

A personalized result merging method for metasearch engine
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Rank aggregation

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cover image ACM Other conferences

ICSCA '17: Proceedings of the 6th International Conference on Software and Computer Applications

February 2017

339 pages

ISBN:9781450348577

DOI:10.1145/3056662

Conference Chairs:
Vitaliy Mezhuyev
Universiti Malaysia Pahang, Malaysia
,
Luigi Benedicenti
University of Regina, Canada

Copyright © 2017 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 February 2017

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National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities of China
Pre-Research Project of the Thirteenth Five-Year-Plan of China

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ICSCA 2017

ICSCA 2017: 2017 6th International Conference on Software and Computer Applications

February 26 - 28, 2017

Bangkok, Thailand

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Cited By

Kumar RBhardwaj RGupta SBalasubramani RVerma M(2025)Performance ranking of metasearch engines based on recall and re-ranking aggregations approachPerformance Measurement and Metrics10.1108/PMM-12-2024-0061Online publication date: 4-Mar-2025
https://doi.org/10.1108/PMM-12-2024-0061
Akritidis LFevgas ABozanis PManolopoulos Y(2022)An unsupervised distance-based model for weighted rank aggregation with list pruningExpert Systems with Applications10.1016/j.eswa.2022.117435202(117435)Online publication date: Sep-2022
https://doi.org/10.1016/j.eswa.2022.117435
Wang MLi QLin Y(2020)An Organizational Structure and Self-adaptive Mechanism for Holonic Multi-Agent SystemsIEEE Access10.1109/ACCESS.2020.3014694(1-1)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3014694
Dimri NKaul HGupta D(2018)MetaXplorer: an intelligent and adaptable metasearch engine using a novel ordered weighted averaging operatorInternational Journal of System Assurance Engineering and Management10.1007/s13198-018-0746-59:6(1315-1325)Online publication date: 8-Sep-2018
https://doi.org/10.1007/s13198-018-0746-5

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