More Web Proxy on the site http://driver.im/

research-article

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.

References

[1]

G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge Data Engineering, 17(6):734--749, 2005.

Digital Library

[2]

R. A. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval: The Concepts and Technology behind Search. Addison-Wesley Professional, 2011.

Digital Library

[3]

T. Berners-Lee, Y. Chen, L. Chilton, D. Connolly, R. Dhanaraj, J. Hollenbach, A. Lerer, and D. Sheets. Tabulator: Exploring and analyzing linked data on the semantic web. In In Procedings of the 3rd International Semantic Web User Interaction Workshop (SWUI06), 2006.

[4]

C. Bizer, T. Heath, and T. Berners-Lee. Linked data - the story so far. Int. J. Semantic Web Inf. Syst, 5(3):1--22, 2009.

[5]

C. Bizer, J. Lehmann, G. Kobilarov, S. Auer, C. Becker, R. Cyganiak, and S. Hellmann. Dbpedia - a crystallization point for the web of data. Web Semant., 7:154--165, September 2009.

Digital Library

[6]

K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor. Freebase: a collaboratively created graph database for structuring human knowledge. In SIGMOD "08, pages 1247--1250. ACM, 2008.

Digital Library

[7]

S. Debnath, N. Ganguly, and P. Mitra. Feature weighting in content based recommendation system using social network analysis. In Proceedings of the 17th international conference on World Wide Web, WWW "08, pages 1041--1042, New York, NY, USA, 2008. ACM.

Digital Library

[8]

Z. Eidoon, N. Yazdani, and F. Oroumchian. A vector based method of ontology matching. In Proc. of 3rd Int. Conf. on Semantics, Knowledge and Grid, pages 378--381, 2007.

Digital Library

[9]

T. Franz, A. Schultz, S. Sizov, and S. Staab. Triplerank: Ranking semantic web data by tensor decomposition. In Proc. of the 8th ISWC, ISWC "09, pages 213--228, 2009.

Digital Library

[10]

M. Garden and G. Dudek. Semantic feedback for hybrid recommendations in recommendz. In IEEE Int. Conf. EEE'05, pages 754--759, 2005.

Digital Library

[11]

J. Golbeck and J. Hendler. Filmtrust: Movie recommendations using trust in web-based social networks. In Proceedings of the IEEE CCNC, 2006.

[12]

O. Hassanzadeh and M. P. Consens. Linked Movie Data Base. In Proceedings of the WWW2009 Workshop on Linked Data on the Web, April 2009.

[13]

B. Heitmann and C. Hayes. Using linked data to build open, collaborative recommender systems. In AAAI Spring Symposium: Linked Data Meets Artificial Intelligence, 2010.

[14]

J. Herlocker, J. A. Konstan, and J. Riedl. Explaining collaborative filtering recommendations. In Proceeding on the ACM 2000 Conference on Computer Supported Cooperative Work, pages 241--250, 2000.

Digital Library

[15]

P. Lops, M. de Gemmis, and G. Semeraro. Content-based recommender systems: State of the art and trends. In Recommender Systems Handbook, pages 73--105. 2011.

[16]

C. D. Manning, P. Raghavan, and H. Schütze. Introduction to Information Retrieval. Cambridge University Press, New York, NY, USA, 2008.

Digital Library

[17]

A. Passant. dbrec: music recommendations using dbpedia. In Proc. of 9th Int. Sem. Web Conf., ISWC'10, pages 209--224, 2010.

Digital Library

[18]

A. Passant and S. Decker. Hey! ho! let's go! explanatory music recommendations with dbrec. In ESWC (2), pages 411--415, 2010.

Digital Library

[19]

P. Perny and J. Zucker. Preference-based search and machine learning for collaborative filtering: the film-consei recommender system. Information, Interaction, Intelligence, 1:9--48, 2001.

[20]

F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors. Recommender Systems Handbook. Springer, 2011.

Digital Library

[21]

G. Salton, A. Wong, and C. S. Yang. A vector space model for automatic indexing. Commun. ACM, 18:613--620, November 1975.

Digital Library

[22]

N. Shabir and C. Clarke. Using linked data as a basis for a learning resource recommendation system. In Learning in the Synergy of Multiple Disciplines, Proceedings of EC-TEL'09, 2009.

[23]

M. Srinivas and L. Patnaik. Genetic algorithms: a survey. Computer, 27(6):17--26, jun 1994.

Digital Library

[24]

M. Szomszor, C. Cattuto, H. Alani, K. O'Hara, A. Baldassarri, V. Loreto, and V. D. Servedio. Folksonomies, the semantic web, and movie recommendation. In 4th ESWC, 2007.

[25]

R. Tous and J. Delgado. A vector space model for semantic similarity calculation and owl ontology alignment. In DEXA, pages 307--316, 2006.

Digital Library

[26]

C. Wartena, W. Slakhorst, and M. Wibbels. Selecting keywords for content based recommendation. In CIKM, pages 1533--1536, 2010.

Digital Library

[27]

R. W. White and R. A. Roth. Exploratory Search: Beyond the Query-Response Paradigm. Synthesis Lectures on Information Concepts, Retrieval, and Services, 1(1):1--98, Jan. 2009.

Digital Library

[28]

M. Zanker, S. Gordea, M. Jessenitschnig, and M. Schnabl. A hybrid similarity concept for browsing semi-structured product items. In EC-Web, volume 4082 of Lecture Notes in Computer Science, pages 21--30. Springer, 2006.

Digital Library

Cited By

Ibrahim OYounis EMohamed EIsmail W(2025)Revisiting recommender systems: an investigative surveyNeural Computing and Applications10.1007/s00521-024-10828-537:4(2145-2173)Online publication date: 4-Jan-2025
https://doi.org/10.1007/s00521-024-10828-5
Jangid MKumar R(2024)Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic reviewMultimedia Tools and Applications10.1007/s11042-024-20262-384:5(2293-2325)Online publication date: 16-Oct-2024
https://doi.org/10.1007/s11042-024-20262-3
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
Show More Cited By

Index Terms

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

Recommendations

Exploiting the web of data in model-based recommender systems
RecSys '12: Proceedings of the sixth ACM conference on Recommender systems

The availability of a huge amount of interconnected data in the so called Web of Data (WoD) paves the way to a new generation of applications able to exploit the information encoded in it. In this paper we present a model-based recommender system ...
Exploiting Linked Open Data in Cold-start Recommendations with Positive-only Feedback
CERI '16: Proceedings of the 4th Spanish Conference on Information Retrieval

In recommender systems, user preferences can be acquired either explicitly by means of ratings, or implicitly --e.g., by processing text reviews, and by mining item browsing and purchasing records. Most existing collaborative filtering approaches have ...
Mapping the central LOD ontologies to PROTON upper-level ontology
OM'10: Proceedings of the 5th International Conference on Ontology Matching - Volume 689

Linking Open Data (LOD) facilitates the emergence of a web of linked data by publishing and interlinking open data on the web in RDF. One can explore linked data across servers by following the links in the graph. The LOD cloud has 203 datasets and more ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 September 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

HP Labs

Conference

I-SEMANTICS '12

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

September 5 - 7, 2012

Graz, Austria

Acceptance Rates

Overall Acceptance Rate 40 of 182 submissions, 22%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

171
Total Citations
View Citations
2,139
Total Downloads

Downloads (Last 12 months)70
Downloads (Last 6 weeks)14

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ibrahim OYounis EMohamed EIsmail W(2025)Revisiting recommender systems: an investigative surveyNeural Computing and Applications10.1007/s00521-024-10828-537:4(2145-2173)Online publication date: 4-Jan-2025
https://doi.org/10.1007/s00521-024-10828-5
Jangid MKumar R(2024)Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic reviewMultimedia Tools and Applications10.1007/s11042-024-20262-384:5(2293-2325)Online publication date: 16-Oct-2024
https://doi.org/10.1007/s11042-024-20262-3
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
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten