Article

Using hierarchical clustering for learning theontologies used in recommendation systems

Authors:

Vincent Schickel-Zuber,

Boi FaltingsAuthors Info & Claims

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 599 - 608

https://doi.org/10.1145/1281192.1281257

Published: 12 August 2007 Publication History

Abstract

Ontologies are being successfully used to overcome semanticheterogeneity, and are becoming fundamental elements of the SemanticWeb. Recently, it has also been shown that ontologies can be used tobuild more accurate and more personalized recommendation systems byinferencing missing user's preferences. However, these systemsassume the existence of ontologies, without considering theirconstruction. With product catalogs changing continuously, newtechniques are required in order to build these ontologies in realtime, and autonomously from any expert intervention.This paper focuses on this problem and show that it is possible tolearn ontologies autonomously by using clustering algorithms. Results on the MovieLens and Jester data sets show that recommendersystem with learnt ontologies significantly outperform the classical recommendation approach.

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Cited By

Abualigah LElaziz MYousri DAl-qaness MEwees AZitar R(2022)Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clusteringJournal of Intelligent Manufacturing10.1007/s10845-022-02016-w34:8(3523-3561)Online publication date: 21-Sep-2022
https://doi.org/10.1007/s10845-022-02016-w
Silva-Palacios DFerri CRamírez-Quintana M(2018)Probabilistic class hierarchies for multiclass classificationJournal of Computational Science10.1016/j.jocs.2018.01.00626(254-263)Online publication date: May-2018
https://doi.org/10.1016/j.jocs.2018.01.006
Silva-Palacios DFerri CRamírez-Quintana M(2017)Improving Performance of Multiclass Classification by Inducing Class HierarchiesProcedia Computer Science10.1016/j.procs.2017.05.218108(1692-1701)Online publication date: 2017
https://doi.org/10.1016/j.procs.2017.05.218
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Index Terms

Using hierarchical clustering for learning theontologies used in recommendation systems
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2007

1080 pages

ISBN:9781595936097

DOI:10.1145/1281192

General Chair:
Pavel Berkhin
Yahoo!, USA
,
Program Chairs:
Rich Caruana
Cornell University, USA
,
Xindong Wu
University of Vermont, USA

Copyright © 2007 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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Published: 12 August 2007

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KDD07

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KDD07: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 15, 2007

California, San Jose, USA

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KDD '07 Paper Acceptance Rate 111 of 573 submissions, 19%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Citations

Cited By

Abualigah LElaziz MYousri DAl-qaness MEwees AZitar R(2022)Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clusteringJournal of Intelligent Manufacturing10.1007/s10845-022-02016-w34:8(3523-3561)Online publication date: 21-Sep-2022
https://doi.org/10.1007/s10845-022-02016-w
Silva-Palacios DFerri CRamírez-Quintana M(2018)Probabilistic class hierarchies for multiclass classificationJournal of Computational Science10.1016/j.jocs.2018.01.00626(254-263)Online publication date: May-2018
https://doi.org/10.1016/j.jocs.2018.01.006
Silva-Palacios DFerri CRamírez-Quintana M(2017)Improving Performance of Multiclass Classification by Inducing Class HierarchiesProcedia Computer Science10.1016/j.procs.2017.05.218108(1692-1701)Online publication date: 2017
https://doi.org/10.1016/j.procs.2017.05.218
Nie LSong XChua T(2016)Learning from Multiple Social NetworksSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S00714ED1V01Y201603ICR0488:2(1-118)Online publication date: 21-Apr-2016
https://doi.org/10.2200/S00714ED1V01Y201603ICR048
Safaeipour HZarandi MBastani S(2016)Crisp to fuzzy ontology conversion in the context of social networks: A new approach2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)10.1109/NAFIPS.2016.7851633(1-6)Online publication date: Oct-2016
https://doi.org/10.1109/NAFIPS.2016.7851633
Tian SOu L(2016)An Improved Slope One Algorithm Combining KNN Method Weighted by User SimilarityWeb-Age Information Management10.1007/978-3-319-47121-1_8(88-98)Online publication date: 15-Oct-2016
https://doi.org/10.1007/978-3-319-47121-1_8
Song XNie LZhang LLiu MChua T(2015)Interest inference via structure-constrained multi-source multi-task learningProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832415.2832578(2371-2377)Online publication date: 25-Jul-2015
https://dl.acm.org/doi/10.5555/2832415.2832578
Lee HKwon S(2014)Ontological semantic inference based on cognitive mapExpert Systems with Applications: An International Journal10.1016/j.eswa.2013.10.02941:6(2981-2988)Online publication date: 1-May-2014
https://dl.acm.org/doi/10.1016/j.eswa.2013.10.029
Hill FKorhonen ABentz C(2013)A Quantitative Empirical Analysis of the Abstract/Concrete DistinctionCognitive Science10.1111/cogs.1207638:1(162-177)Online publication date: 13-Aug-2013
https://doi.org/10.1111/cogs.12076
Beydoun GGarcíA-SáNchez FVincent-Torres CLopez-Lorca AMartíNez-BéJar R(2013)Providing metrics and automatic enhancement for hierarchical taxonomiesInformation Processing and Management: an International Journal10.1016/j.ipm.2012.01.00649:1(67-82)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1016/j.ipm.2012.01.006
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