Skip to main content
SpringerLink
Log in
Book cover

Trends in Practical Applications of Agents and Multiagent Systems pp 181–188Cite as

Multi-label Classification for Recommender Systems

  • Conference paper

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 221))

Abstract

Multi-label classification groups a set of supervised learning methods producing models capable of classifying examples in more than one class. These methods have been applied in diverse fields; however, the field of recommender systems has been hardly explored. In this work, books’ recommendation data are used to evaluate the behavior of the main multi-label classification methods in this application domain. The experiments carried out demonstrated their suitability to provide reliable recommendations and to avoid the grey sheep problem.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aljaber, B., Martinez, D., Stokes, N., Bailey, J.: Improving mesh classificationof biomedical articles using citation contexts. Journal of Biomedical Informatics 44(5), 881–896 (2011)

    Article  Google Scholar 

  2. Cerri, R., Carvalho, A.C.P.L.F.: Hierarchical multilabel classification usingtop-down label combination and artificial neural networks. In: 2010 Eleventh Brazilian Symposium on Neural Networks (SBRN), pp. 253–258 (October 2010)

    Google Scholar 

  3. Cho, H.C., Kim, J.K., Kim, S.H.: A Personalized Recommender System Based on Web Usage Mining and Decision Tree Induction. Expert Systems with Applications 23, 329–342 (2002)

    Article  Google Scholar 

  4. Clare, A., King, R.D.: Knowledge Discovery in Multi-label Phenotype Data. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 42–53. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  5. Fürnkranz, J., Hüllermeier, E., Mencia, E.L., Brinker, K.: Multilabel classification via calibrated label ranking. Machine Learning 73(2), 133 (2008)

    Article  Google Scholar 

  6. Ghamrawi, N., McCallum, A.: Collective multi-label classification. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, CIKM 2005, vol. 189, p. 195. ACM Press (2005)

    Google Scholar 

  7. Hüllermeier, E., Fürnkranz, J., Cheng, W., Brinker, K.: Label ranking by learning pairwise preferences. Artificial Intelligence 172(16-17), 1897–1916 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  8. Lee, C.H., Kim, Y.H., Rhee, P.K.: Web Personalization Expert with Combining collaborative Filtering and association Rule Mining Technique. Expert Systems with Applications 21, 131–137 (2001)

    Article  Google Scholar 

  9. McCallum, A.K.: Multi-label text classification with a mixture modeltrained by em. In: AAAI 1999 Workshop on Text Learning (1999)

    Google Scholar 

  10. Mencia, E.L., Furnkranz, J.: Pairwise learning of multilabel classifications withperceptrons. In: IEEE International Joint Conference on Neural Networks, IJCNN 2008 (IEEE World Congress on Computational Intelligence), pp. 2899–2906 (June 2008)

    Google Scholar 

  11. Névéol, A., Shooshan, S.E., Humphrey, S.M., Mork, J.G., Aronson, A.R.: Arecent advance in the automatic indexing of the biomedical literature. Journal of Biomedical Informatics 42(5), 814–823 (2009)

    Article  Google Scholar 

  12. Quevedo, J.R., Montañes, E., Ranilla, J., Díaz, I.: TagR: Un Sistema de Recomendación de Etiquetas basado en Regresión Logística. In: I Congreso Español de Recuperación de Información (CERI 2010), Madrid, España, de junio 15 y 16, pp. 53–63 (2010)

    Google Scholar 

  13. Rak, R., Kurgan, L.A., Reformat, M.: Multilabel associative classification categorization of medlineaticles into mesh keywords. IEEE Engineering in Medicine and Biology Magazine 26(2), 47–55 (2007)

    Article  Google Scholar 

  14. Resnick, P., Iacovou, N., Suchack, M., Bergstrom, P., Riedl, J.: Grouplens: An open architecture for collaborative filtering of netnews. In: Proc. of ACM CSW 1994 Conference on Computer. Supported Cooperative Work, pp. 175–186 (1994)

    Google Scholar 

  15. Schafer, J.B., Konstant, J.A., Riedl, J.: E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery 5, 115–153 (2001)

    Article  MATH  Google Scholar 

  16. Spyromitros, E., Tsoumakas, G., Vlahavas, I.P.: An Empirical Study of Lazy Multilabel Classification Algorithms. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds.) SETN 2008. LNCS (LNAI), vol. 5138, pp. 401–406. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  17. Thabtah, F.A., Cowling, P., Peng, Y.: MMAC: A New Multi-Class, Multi-Label Associative Classification Approach. In: Fourth IEEE InternationalConference on Data Mining, ICDM 2004, pp. 217–224 (2004)

    Google Scholar 

  18. Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.: Multi-label classification of music by emotion. EURASIP Journal on Audio, Speech, and Music Processing 2011(1), 4 (2011)

    Article  Google Scholar 

  19. Tsoumakas, G., Vlahavas, I.P.: Random k-labelsets: An ensemble method for multilabel classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406–417. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  20. Tsoumakas, G., Katakis, I., Vlahavas, I.: Effective and efficient multilabel classification in domains with large number of labels. In: ECML PKDD 2008 Workshop on Mining Multidimensional Data MMD 2008, pp. 30–44 (2008)

    Google Scholar 

  21. Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer US (2010)

    Google Scholar 

  22. Tsoumakas, G., Katakis, I., Vlahavas, I.: Multilabel text classification for automatedtag suggestion. In: Proceedings of the ECML/PKDD 2008 Workshop on Discovery Challenge, vol. 9, pp. 1–9 (2008)

    Google Scholar 

  23. Tsoumakas, G., Katakis, I., Vlahavas, I.: Random k-labelsetsfor multilabel classification. IEEE Transactions on Knowledge and Data Engineering 23(7), 1079–1089 (2011)

    Article  Google Scholar 

  24. Ueda, N., Saito, K.: Parametric mixture models for multi-labeled text. Text 15, 737–744 (2003)

    Google Scholar 

  25. Wei, Z., Miao, D., Zhao, R., Xie, C., Zhang, Z.: Visualizing search results basedon multi-label classification. In: IEEE International Conference on Progress in Informatics and Computing (PIC), vol. 1, pp. 203–207 (December 2010)

    Google Scholar 

  26. Wieczorkowska, A., Synak, P., Ras, Z.W.: Multi-label classification of emotions in music. In: Klopotek, M.A., Wierzchon, S.T., Trojanowski, K. (eds.) IIPWM 2006. ASC, vol. 35, pp. 307–315. Springer, Heidelberg (2006)

    Google Scholar 

  27. Zhang, M.L., Zhou, Z.H.: ML-KNN: A lazy learning approach to multi label learning. Pattern Recognition 40(7), 2038–2048 (2007)

    Article  MATH  Google Scholar 

  28. Zhang, M.L., Zhou, Z.H.: Multilabel Neural Networks with Applicationsto Functional Genomics and Text Categorization. IEEE Transactions on Knowledge and Data Engineering 18(10), 1338–1351 (2006)

    Article  Google Scholar 

  29. Zhang, M.L.: ML-RBF: RRBF Neural Networks for Multi-Label Learning. Neural Processing Letters 29, 61–74 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Computing and Automatic, University of Salamanca, Plaza de los Caídos s/n, 37008, Salamanca, Spain

    Dolly Carrillo, Vivian F. López & María N. Moreno

Authors
  1. Dolly Carrillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vivian F. López
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. María N. Moreno
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dolly Carrillo .

Editor information

Editors and Affiliations

  1. Escuela Universitaria de Informática, Universidad Pontificia de Salamanca, Salamanca, Spain

    Javier Bajo Pérez

  2. Dept. of Computing Sci. and Control, University of Salamanca, Salamanca, Spain

    Juan M. Corchado Rodríguez

  3. Technische Universität Berlin, Berlin, Germany

    Johannes Fähndrich

  4. Technologies de Lille, Université des Sciences et Technologies de Lille, Villeneuve d’Ascq, France

    Philippe Mathieu

  5. Department of Computer Science, Dartmouth College, Hanover, New Hampshire, USA

    Andrew Campbell

  6. Ontology Engineering Group, Universidad Politécnica de Madrid Facultad de Informática, Boadilla del Monte, Madrid, Spain

    Mari Carmen Suarez-Figueroa

  7. Departamento de Ingeniería Informática, Ciudad Universitaria de Cantoblanco, Madrid, Spain

    Alfonso Ortega

  8. LAMIH (UMR CNRS 8530), Universite de Valenciennes, Valenciennes, France

    Emmanuel Adam

  9. Departamento de Informática, University of Castilla-La Mancha, Albacete, Albacete, Spain

    Elena Navarro

  10. Centre for Intelligent Information, University Rey Juan Carlos Madrid (URJC), Móstoles, Madrid, Spain

    Ramon Hermoso

  11. Dept. de Informática y Automática, Universidad de Salamanca, Salamanca, Salamanca, Spain

    María N. Moreno

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

Carrillo, D., López, V.F., Moreno, M.N. (2013). Multi-label Classification for Recommender Systems. In: Pérez, J., et al. Trends in Practical Applications of Agents and Multiagent Systems. Advances in Intelligent Systems and Computing, vol 221. Springer, Cham. https://doi.org/10.1007/978-3-319-00563-8_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-00563-8_22

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-00562-1

  • Online ISBN: 978-3-319-00563-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation