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Linked open data to support content-based recommender systems

Authors:

Tommaso Di Noia,

Roberto Mirizzi,

Vito Claudio Ostuni,

Markus ZankerAuthors Info & Claims

I-SEMANTICS '12: Proceedings of the 8th International Conference on Semantic Systems

Pages 1 - 8

https://doi.org/10.1145/2362499.2362501

Published: 05 September 2012 Publication History

Abstract

The World Wide Web is moving from a Web of hyper-linked Documents to a Web of linked Data. Thanks to the Semantic Web spread and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets. These datasets are connected with each other to form the so called Linked Open Data cloud. As of today, there are tons of RDF data available in the Web of Data, but only few applications really exploit their potential power. In this paper we show how these data can successfully be used to develop a recommender system (RS) that relies exclusively on the information encoded in the Web of Data. We implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users. We extensively evaluated the approach and validated the effectiveness of the algorithms by experimentally measuring their accuracy with precision and recall metrics.

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Cited By

Bellini VDi Sciascio EDonini FPomo CRagone ASchiavone A(2024)A qualitative analysis of knowledge graphs in recommendation scenarios through semantics-aware autoencodersJournal of Intelligent Information Systems10.1007/s10844-023-00830-z62:3(787-807)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10844-023-00830-z
Kar PRoy MDatta SKar PRoy MDatta S(2024)Collaborative Filtering and Content-Based SystemsRecommender Systems: Algorithms and their Applications10.1007/978-981-97-0538-2_3(19-30)Online publication date: 12-Jun-2024
https://doi.org/10.1007/978-981-97-0538-2_3
Ismail HHussein NHarous SKhalil A(2023)Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User ModelsEducation Sciences10.3390/educsci1307074113:7(741)Online publication date: 20-Jul-2023
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Index Terms

Linked open data to support content-based recommender systems
1. Information systems
  1. Information retrieval

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Information

Published In

cover image ACM Other conferences

I-SEMANTICS '12: Proceedings of the 8th International Conference on Semantic Systems

September 2012

215 pages

ISBN:9781450311120

DOI:10.1145/2362499

Editors:
Harald Sack
Hasso-Plattner-Institut für Softwaresystemtechnik GmbH
,
Tassilo Pellegrini
University of Applied Sciences St. Pölten
,
Program Chairs:
Valentina Presutti
Institute for Cognitive Science and Technology, Rome - Italy
,
H. Sofia Pinto
Technical University of Lisbon - Portugal

Copyright © 2012 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: 05 September 2012

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HP Labs

Conference

I-SEMANTICS '12

I-SEMANTICS '12: I-SEMANTICS 2012 - 8th International Conference on Semantic Systems

September 5 - 7, 2012

Graz, Austria

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Overall Acceptance Rate 40 of 182 submissions, 22%

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Cited By

Bellini VDi Sciascio EDonini FPomo CRagone ASchiavone A(2024)A qualitative analysis of knowledge graphs in recommendation scenarios through semantics-aware autoencodersJournal of Intelligent Information Systems10.1007/s10844-023-00830-z62:3(787-807)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10844-023-00830-z
Kar PRoy MDatta SKar PRoy MDatta S(2024)Collaborative Filtering and Content-Based SystemsRecommender Systems: Algorithms and their Applications10.1007/978-981-97-0538-2_3(19-30)Online publication date: 12-Jun-2024
https://doi.org/10.1007/978-981-97-0538-2_3
Ismail HHussein NHarous SKhalil A(2023)Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User ModelsEducation Sciences10.3390/educsci1307074113:7(741)Online publication date: 20-Jul-2023
https://doi.org/10.3390/educsci13070741
Ferrara AAnelli VMancino ADi Noia TDi Sciascio E(2023)KGFlex: Efficient Recommendation with Sparse Feature Factorization and Knowledge GraphsACM Transactions on Recommender Systems10.1145/35889011:4(1-30)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1145/3588901
Tao CLin KHuang ZSun X(2023)CRAM: Code Recommendation With Programming Context Based on Self-Attention MechanismIEEE Transactions on Reliability10.1109/TR.2022.317130972:1(302-316)Online publication date: Mar-2023
https://doi.org/10.1109/TR.2022.3171309
Seo DYoo Y(2023)Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon IssuanceIEEE Access10.1109/ACCESS.2023.323942511(7924-7932)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3239425
Chansanam WChotkamonsawad PThi Nguyen L(2023)Developing a Knowledge Organization System for Ethnic Groups in Lao PDR through Linked Open Data TechniquesJournal of the Australian Library and Information Association10.1080/24750158.2023.227509673:1(84-97)Online publication date: 16-Nov-2023
https://doi.org/10.1080/24750158.2023.2275096
Gupta AShrinath P(2023)Link Prediction based on bipartite graph for recommendation system using optimized SVD++Procedia Computer Science10.1016/j.procs.2023.01.114218:C(1353-1365)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.procs.2023.01.114
Kleemann TZiegler J(2023)Blending Conversational Product Advisors and Faceted Filtering in a Graph-Based ApproachHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42286-7_8(137-159)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-42286-7_8
Wang MGao FGu J(2022)Process-Aware Dialogue System With Clinical Guideline KnowledgeInternational Journal of Web Services Research10.4018/IJWSR.30439219:1(1-22)Online publication date: 10-Jun-2022
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