research-article

A personalized recommender system based on users' information in folksonomies

Authors:

Mohamed Nader Jelassi,

Sadok Ben Yahia,

Engelbert Mephu NguifoAuthors Info & Claims

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

Pages 1215 - 1224

https://doi.org/10.1145/2487788.2488151

Published: 13 May 2013 Publication History

Abstract

Thanks to the high popularity and simplicity of folksonomies, many users tend to share objects (movies, songs, bookmarks, etc.) by annotating them with a set of tags of their own choice. Users represent the core of the system since they are both the contributors and the creators of the information. Yet, each user has its own profile and its own ideas making thereby the strength as well as the weakness of folksonomies. Indeed, it would be helpful to take account of users' profile when suggesting a list of tags and resources or even a list of friends, in order to make a more personal recommandation. The goal is to suggest tags (or resources) which may correspond to a user's vocabulary or interests rather than a list of most used and popular tags in folksonomies. In this paper, we consider users' profile as a new dimension of a folksonomy classically composed of three dimensions "users, tags, ressources" and we propose an approach to group users with equivalent profiles and equivalent interests as quadratic concepts. Then, we use quadratic concepts in order to propose our personalized recommendation system of users, tags and resources according to each user's profile. Carried out experiments on the large-scale real-world filmography dataset MovieLens highlight encouraging results in terms of precision.

References

[1]

F. Abel, Q. Gao, G.-J. Houben, and K. Tao. Analyzing Temporal Dynamics in Twitter Profiles for Personalized Recommendations in the Social Web. In Proceedings of ACM WebSci '11, Koblenz, Germany, 2011.

Digital Library

[2]

R. Agrawal and R. Srikant. Mining sequential patterns. In Proc. of the ICDE, ICDE '95, pages 3--14, Washington, DC, USA, 1995. IEEE Computer Society.

Digital Library

[3]

H. Alves and A. Santanchè. Abstract framework for social ontologies and folksonomized ontologies. In Proceedings of the 4th International Workshop on Semantic Web Information Management, SWIM '12, pages 10:1--10:4, New York, NY, USA, 2012. ACM.

Digital Library

[4]

S. Amer-Yahia. Recommendation projects at yahoo! IEEE Data Eng. Bull., 34:69--77, 2011.

[5]

K. Bischoff, C. S. Firan, W. Nejdl, and R. Paiu. Can all tags be used for search? In Proc. of the 17th ACM CIKM 2008, pages 193--202, Napa Valley, California, 2008. ACM Press.

Digital Library

[6]

C. Cattuto, C. Schmitz, A. Baldassarri, A. Servedio, V. Loreto, A. Hotho, M. Grahl, and G. Stumme. Network properties of folksonomies. In Proc. of AICSI on NANSE, Amsterdam, The Netherlands, pages 245--262, 2007.

[7]

L. Cerf, J. Besson, C. Robardet, and J.-F. Boulicaut. Closed patterns meet n-ary relations. ACM TKDD, 3:3:1--3:36, March 2009.

Digital Library

[8]

M. Das, S. Thirumuruganathan, S. Amer-Yahia, G. Das, and C. Yu. Who tags what? an analysis framework. In Proceedings of PVLDB, 5(11):1567--1578, 2012.

Digital Library

[9]

W. Dedzoe. Traitement de Requêtes Top-k dans les Communautés Virtuelles P2P de Partage de Données. These, Université de Nantes, Nov. 2011.

[10]

J. Diederich and T. Iofciu. Finding communities of practice from user profiles based on folksonomies. In Proceedings of the 1st International Workshop on TEL-CoPs, Crete, Greece, pages 288--297, 2006.

[11]

M. Dubinko, R. Kumar, J. Magnani, J. Novak, P. Raghavan, and A. Tomkins. Visualizing tags over time. ACM Trans. Web, 1(2):193--202, 2007.

Digital Library

[12]

L. Fang, A. Fabrikant, and K. LeFevre. Look who i found: understanding the effects of sharing curated friend groups. In Proc. of the 3rd Annual ACM Web Science Conference, WebSci '12, pages 95--104, New York, NY, USA, 2012.

Digital Library

[13]

Z. Guan, J. Bu, Q. Mei, C. Chen, and C. Wang. Personalized tag recommendation using graph-based ranking on multi-type interrelated objects. In Proceedings of the 32nd international ACM SIGIR, pages 540--547, New York, NY, USA, 2009. ACM.

Digital Library

[14]

A. Hotho. Data mining on folksonomies. In G. Armano, M. de Gemmis, G. Semeraro, and E. Vargiu, editors, Intelligent Information Access, volume 301 of Studies in Computational Intelligence, pages 57--82. Springer, Berlin / Heidelberg, 2010.

[15]

A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme. Information retrieval in folksonomies: Search and ranking. In Proc. of ESWC, Budva, Montenegro, volume 4011 of LNCS, pages 411--426. Springer, Heidelberg, 2006.

Digital Library

[16]

J. Hu, B. Wang, and Z. Tao. Personalized tag recommendation using social contacts. In Proceedings of Workshop SRS'11, in conjunction with CSCW, 2011.

[17]

R. Jäschke, A. Hotho, C. Schmitz, B. Ganter, and G. Stumme. Discovering shared conceptualizations in folksonomies. Web Semantics., 6:38--53, 2008.

Digital Library

[18]

R. Jäschke, L. Marinho, A. A. Hotho, S.-T. Lars, and G. Stum. Tag recommendations in folksonomies. In Proc. of the 11th ECML PKDD, Warsaw, Poland, pages 506--514, 2007.

Digital Library

[19]

M. N. Jelassi. A quadratic approach for trend detection in folksonomies. In Proceedings of the 6th RR, Vienna, Austria, pages 278--283, 2012.

Digital Library

[20]

M. N. Jelassi, S. Ben Yahia, and E. Mephu Nguifo. A scalable mining of frequent quadratic concepts in d-folksonomies. ArXiv e-prints, Dec. 2012.

[21]

N. Landia and S. Anand. Personalised tag recommendation. Recommender Systems & the Social Web, New York, NY, USA, pages 83--86, 2009.

[22]

H. Liang. User profiling based on folksonomy information in Web 2.0 for personalized recommender systems. PhD thesis, Queensland University of Technology, 2010.

[23]

H. Liang, Y. Xu, Y. Li, and R. Nayak. Personalized recommender system based on item taxonomy and folksonomy. In Proceedings of the 19th ACM CIKM'10, pages 1641--1644, New York, NY, USA, 2010. ACM.

Digital Library

[24]

M. Lipczak. Tag recommendation for folksonomies oriented towards individual users. In Proc. of the ECML PKDD Discovery Challenge, Antwerp, Belgium, 2008.

[25]

E. Michlmayr. Learning user profiles from tagging data and leveraging them for personal(ized) information access. In Proceedings of the 16th WWW, 2007.

[26]

E. Michlmayr and S. Cayzer. Learning user profiles from tagging data and leveraging them for personal(ized) information access. In Proc. of the Workshop on Tagging and Metadata for Social Information Organization in the 16th WWW, Banff, Alberta, Canada, 2007.

[27]

P. Mika. Ontologies are us: A unified model of social networks and semantics. Web Semantics., 5(1):5--15, 2007.

Digital Library

[28]

M. Noll, G. Michael, and C. Meinel. Web search personalization via social bookmarking and tagging. In Proc. of the 6th ISWC/ASWC, Busan, Korea, pages 367--380, 2007.

Digital Library

[29]

C. Penet, C.-H. Demarty, G. Gravier, and P. Gros. De la détection d'évènements sonores violents par SVM dans les films. In Proc. of the 13th ORASIS - Congrès des jeunes chercheurs en vision par ordinateur, Praz-sur-Arly, France, 2011.

[30]

E. Tonkin and M. Guy. Folksonomies: Tidying up tags? D-Lib, volume 12(1), 2006.

[31]

C. Trabelsi, N. Jelassi, and S. Ben Yahia. Scalable mining of frequent tri-concepts. In Proc. of the 15th PAKDD, Kuala Lampur, Malaysia, pages 231--242, 2012.

Digital Library

[32]

C. Trabelsi, N. Jelassi, and S. B. Yahia. Auto-complétion de requêtes par une base générique de règles d'association triadiques. In G. Pasi and P. Bellot, editors, CORIA, pages 9--24. Éditions Universitaires d'Avignon, 2011.

[33]

D. Vallet, I. Cantador, and J. M. Jose. Personalizing web search with folksonomy-based user and document profiles. In Proceedings of the 32nd ECIR, pages 420--431, Berlin, Heidelberg, 2010. Springer-Verlag.

Digital Library

Cited By

Raju BUmmesalma M(2022)Demography-Based Hybrid Recommender System for Movie RecommendationsSustainable Advanced Computing10.1007/978-981-16-9012-9_5(49-58)Online publication date: 31-Mar-2022
https://doi.org/10.1007/978-981-16-9012-9_5
Egurnov DIgnatov DTochilkin D(2021)Triclustering in Big Data SettingComplex Data Analytics with Formal Concept Analysis10.1007/978-3-030-93278-7_11(239-258)Online publication date: 8-Dec-2021
https://doi.org/10.1007/978-3-030-93278-7_11
Barman DSarkar RTudu AChowdhury N(2020)Personalized query recommendation system : A genetic algorithm approachJournal of Interdisciplinary Mathematics10.1080/09720502.2020.173196423:2(523-535)Online publication date: 12-May-2020
https://doi.org/10.1080/09720502.2020.1731964
Show More Cited By

Index Terms

A personalized recommender system based on users' information in folksonomies
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

Recommendations

On a serendipity-oriented recommender system based on folksonomy

This paper proposes a recommendation method that focuses on not only predictive accuracy but also serendipity. On many of the conventional recommendation methods, items are categorized according to their attributes (a genre, an authors, etc.) by the ...
Personalized recommender system based on item taxonomy and folksonomy
CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

Item folksonomy or tag information is popularly available on the web now. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to ...
Exploiting Social Tagging in a Web 2.0 Recommender System

Recommender systems help users cope with information overload by using their preferences to recommend items. To date, most recommenders have employed users' ratings, information about the user's profile, or metadata describing the items. To take ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

May 2013

1636 pages

ISBN:9781450320382

DOI:10.1145/2487788

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

Copyright © 2013 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
CGIBR: Comite Gestor da Internet no Brazil

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WWW '13

Sponsor:

NICBR
CGIBR

WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

Acceptance Rates

WWW '13 Companion Paper Acceptance Rate 831 of 1,250 submissions, 66%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
356
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)1

Reflects downloads up to 04 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Raju BUmmesalma M(2022)Demography-Based Hybrid Recommender System for Movie RecommendationsSustainable Advanced Computing10.1007/978-981-16-9012-9_5(49-58)Online publication date: 31-Mar-2022
https://doi.org/10.1007/978-981-16-9012-9_5
Egurnov DIgnatov DTochilkin D(2021)Triclustering in Big Data SettingComplex Data Analytics with Formal Concept Analysis10.1007/978-3-030-93278-7_11(239-258)Online publication date: 8-Dec-2021
https://doi.org/10.1007/978-3-030-93278-7_11
Barman DSarkar RTudu AChowdhury N(2020)Personalized query recommendation system : A genetic algorithm approachJournal of Interdisciplinary Mathematics10.1080/09720502.2020.173196423:2(523-535)Online publication date: 12-May-2020
https://doi.org/10.1080/09720502.2020.1731964
Kichou SMellah HBoussaid OMeziane A(2016)Handicraft Women Recommendation Approach Based on User's Social Tagging Operations2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2016.0108(618-621)Online publication date: Oct-2016
https://doi.org/10.1109/WI.2016.0108
Thilakaratne MWeerasinghe RPerera S(2016)Knowledge-Driven Approach to Predict Personality Traits by Leveraging Social Media Data2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2016.0048(288-295)Online publication date: Oct-2016
https://doi.org/10.1109/WI.2016.0048
Li TLiu AHuang C(2016)A Similarity Scenario-Based Recommendation Model With Small Disturbances for Unknown Items in Social NetworksIEEE Access10.1109/ACCESS.2016.26472364(9251-9272)Online publication date: 2016
https://doi.org/10.1109/ACCESS.2016.2647236
Gnatyshak D(2015)A single-pass triclustering algorithmAutomatic Documentation and Mathematical Linguistics10.3103/S000510551501005749:1(27-41)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.3103/S0005105515010057
Jelassi MBen Yahia SMephu Nguifo E(2015)Towards more targeted recommendations in folksonomiesSocial Network Analysis and Mining10.1007/s13278-015-0307-85:1Online publication date: 30-Nov-2015
https://doi.org/10.1007/s13278-015-0307-8
Ignatov DMikhailova MZakirova AMalioukov A(2015)Recommendation of Ideas and Antagonists for Crowdsourcing Platform WitologyInformation Retrieval10.1007/978-3-319-25485-2_9(276-296)Online publication date: 10-Dec-2015
https://doi.org/10.1007/978-3-319-25485-2_9
Roque do Amaral AZaina LRodrigues J(2014)Cataloguing of learning objects using social tagging2014 XL Latin American Computing Conference (CLEI)10.1109/CLEI.2014.6965111(1-9)Online publication date: Sep-2014
https://doi.org/10.1109/CLEI.2014.6965111
Show More Cited By

View Options

Get Access

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.

Media

Figures

Other

Tables

View Table of Contents