research-article

IFME: information filtering by multiple examples with under-sampling in a digital library environment

Authors:

Mingzhu Zhu,

Chao Xu,

Yi-Fang Brook WuAuthors Info & Claims

JCDL '13: Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries

Pages 107 - 110

https://doi.org/10.1145/2467696.2467736

Published: 22 July 2013 Publication History

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Abstract

With the amount of digitalized documents increasing exponentially, it is more difficult for users to keep up to date with the knowledge in their domain. In this paper, we present a framework named IFME (Information Filtering by Multiple Examples) in a digital library environment to help users identify the literature related to their interests by leveraging the Positive Unlabeled learning (PU learning). Using a few relevant documents provided by a user and considering the documents in an online database as unlabeled data (called U), it ranks the documents in U using a PU learning algorithm. From the experimental results, we found that while the approach performed well when a large set of relevant feedback documents were available, it performed relatively poor when the relevant feedback documents were few. We improved IFME by combining PU learning with under-sampling to tune the performance. Using Mean Average Precision (MAP), our experimental results indicated that with under-sampling, the performance improved significantly even when the size of P was small. We believe the PU learning based IFME framework brings insights to develop more effective digital library systems.

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Cited By

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Huang TXie XLiu X(2023)Multi-level Correlation Matching for Legal Text Similarity Modeling with Multiple ExamplesWeb Information Systems Engineering – WISE 202310.1007/978-981-99-7254-8_48(621-632)Online publication date: 21-Oct-2023
https://doi.org/10.1007/978-981-99-7254-8_48
Piccione ABerkery JSabbagh SAndreopoulos Y(2022)Predicting resistive wall mode stability in NSTX through balanced random forests and counterfactual explanationsNuclear Fusion10.1088/1741-4326/ac44af62:3(036002)Online publication date: 18-Jan-2022
https://doi.org/10.1088/1741-4326/ac44af
Shao ZSi FXu ZGuo D(2021)Homogenization Algorithm Based on Incremental L2-Discrepancy Filtering for Data-Driven ModellingArtificial Intelligence in Industry 4.010.1007/978-3-030-61045-6_6(73-83)Online publication date: 28-Feb-2021
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Index Terms

IFME: information filtering by multiple examples with under-sampling in a digital library environment
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

JCDL '13: Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries

July 2013

480 pages

ISBN:9781450320771

DOI:10.1145/2467696

General Chairs:
J. Stephen Downie
University of Illinois at Urbana-Champaign, USA
,
Robert H. McDonald
Indiana University Bloomington, USA
,
Program Chairs:
Timothy W. Cole
University of Illinois at Urbana-Champaign, USA
,
Robert Sanderson
Los Alamos National Laboratory, USA
,
Frank Shipman
Texas A&M University, USA

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: 22 July 2013

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JCDL '13

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JCDL '13: 13th ACM/IEEE-CS Joint Conference on Digital Libraries

July 22 - 26, 2013

Indiana, Indianapolis, USA

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JCDL '13 Paper Acceptance Rate 28 of 95 submissions, 29%;

Overall Acceptance Rate 415 of 1,482 submissions, 28%

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Cited By

View all

Huang TXie XLiu X(2023)Multi-level Correlation Matching for Legal Text Similarity Modeling with Multiple ExamplesWeb Information Systems Engineering – WISE 202310.1007/978-981-99-7254-8_48(621-632)Online publication date: 21-Oct-2023
https://doi.org/10.1007/978-981-99-7254-8_48
Piccione ABerkery JSabbagh SAndreopoulos Y(2022)Predicting resistive wall mode stability in NSTX through balanced random forests and counterfactual explanationsNuclear Fusion10.1088/1741-4326/ac44af62:3(036002)Online publication date: 18-Jan-2022
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Shao ZSi FXu ZGuo D(2021)Homogenization Algorithm Based on Incremental L2-Discrepancy Filtering for Data-Driven ModellingArtificial Intelligence in Industry 4.010.1007/978-3-030-61045-6_6(73-83)Online publication date: 28-Feb-2021
https://doi.org/10.1007/978-3-030-61045-6_6
Lissandrini MMottin DPalpanas TVelegrakis Y(2018)Data Exploration Using Example-Based MethodsSynthesis Lectures on Data Management10.2200/S00881ED1V01Y201810DTM05310:4(1-164)Online publication date: 27-Nov-2018
https://doi.org/10.2200/S00881ED1V01Y201810DTM053
Lin KChang YShen CLin M(2018)Leveraging Online Word of Mouth for Personalized App RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2018.28788665:4(1061-1070)Online publication date: Dec-2018
https://doi.org/10.1109/TCSS.2018.2878866
Mashayekhi MRezaeian IZhang AAnders J(2018)Subscription and Redemption Prediction in Mutual Funds Using Machine Learning Techniques2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8622149(4692-4697)Online publication date: Dec-2018
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Cheng DWu M(2016)A Novel Classifier - Weighted Features Cost-Sensitive SVM2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)10.1109/iThings-GreenCom-CPSCom-SmartData.2016.133(598-603)Online publication date: Dec-2016
https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.133
Zhu MWu YCarterette BDiaz FCastillo CMetzler D(2014)Search by multiple examplesProceedings of the 7th ACM international conference on Web search and data mining10.1145/2556195.2556206(667-672)Online publication date: 24-Feb-2014
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Zhu MXiong WWu Y(2014)Learning to Rank with Only Positive ExamplesProceedings of the 2014 13th International Conference on Machine Learning and Applications10.1109/ICMLA.2014.19(87-92)Online publication date: 3-Dec-2014
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