Skip to main content
SpringerLink
Log in

An adapted incremental graded multi-label classification model for recommendation systems

  • Regular Paper
  • Published:
Progress in Artificial Intelligence Aims and scope Submit manuscript

Abstract

Graded multi-label classification (GMLC) is the task of assigning to each data a set of relevant labels with corresponding membership grades. This paper is interested in GMLC for large and evolving datasets where data are collected from a possibly infinite stream. Many commercial and non-commercial websites acquire such data by giving users the opportunity to rank items any time using an ordinal scale like one-to-five star rating. Typically these collected data are sparse because users rank only a small subset of items. Websites rely on recommender systems to dynamically adapt the recommended item set for each user. Hence, the applied recommender system should remain scalable and efficient when dealing with sparse data. State-of-the-art methods related to GMLC were tested only in batch mode. Their performance in an incremental mode is not investigated, especially in presence of sparse data and concept drifts. This paper presents our proposed incremental GMLC method which answers the above challenges and can be applied to build a recommender system. This method is tested on the well-known MovieLens and Jester datasets, and it is able to adapt to concept drifts and maintain the Hamming loss at a low level.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. http://eigentaste.berkeley.edu/.

References

  1. Aggarwal, C.C.: A survey of stream classification algorithms. In: Aggarwal, C.C. (ed.) Data Classification: Algorithms and Applications, pp. 245–274. CRC Press, Boca Raton (2014)

    Google Scholar 

  2. Agrawal, S., Agrawal, J., Kaur, S., Sharma, S.: A comparative study of fuzzy PSO and fuzzy SVD-based RBF neural network for multi-label classification. Neural Comput. Appl. 1–12 (2016). doi:10.1007/s00521-016-2446-x

  3. Amanda, C., King, R.D.: Knowledge discovery in multi-label phenotype data. In: Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD ’01, pp. 42–53. Springer, London (2001)

  4. Bifet, A., Gavaldă, R.: Learning from time-changing data with adaptive windowing. In: SDM, pp. 443–448. SIAM (2007)

  5. Bobadilla, J., Ortega, F., Hernando, A., GutiéRrez, A.: Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013)

    Article  Google Scholar 

  6. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  7. Brinker, C., Mencía, E.L., Fürnkranz, J.: Graded multilabel classification by pairwise comparisons. In: 2014 IEEE International Conference on Data Mining, pp. 731–736 (2014)

  8. Cardoso, J.S., da Costa, J.F.P.: Learning to classify ordinal data: the data replication method. J. Mach. Learn. Res. 8, 1393–1429 (2007)

    MathSciNet  MATH  Google Scholar 

  9. Chen, C.L., Chang, C.H.: Evaluation of session-based recommendation systems for social networks. In: 2013 IEEE 13th International Conference on Data Mining Workshops, pp. 758–765 (2013)

  10. Cheng, W., Dembczynski, K., Hüllermeier, E.: Graded multilabel classification: the ordinal case. In: ICML, pp. 223–230 (2010)

  11. Clark, P., Niblett, T.: The CN2 induction algorithm. Mach. Learn. 3(4), 261–283 (1989)

    Google Scholar 

  12. Cohen, E., Strauss, M.J.: Maintaining time-decaying stream aggregates. J. Algorithms 59(1), 19–36 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  13. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. J. R. Stat. Soc. Ser. B 39(1), 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  14. Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’00, pp. 71–80. ACM, New York (2000)

  15. Eibe, F., Mark, H.: A simple approach to ordinal classification. In: Proceedings of the 12th European Conference on Machine Learning, EMCL ’01, pp. 145–156. Springer, London (2001)

  16. Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum. Comput. Interact. 4(2), 81–173 (2011)

    Article  Google Scholar 

  17. Fürnkranz, J., Widmer, G.: Incremental reduced error pruning. In: Cohen W.W., Hirsh H. (eds.) Proceedings of the 11th International Conference on Machine Learning (ML-94), pp. 70–77. Morgan Kaufmann, New Brunswick (1994)

  18. Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: A Survey of Classification Methods in Data Streams. Springer, Boston (2007)

    Book  MATH  Google Scholar 

  19. Gama, J.A., Sebastião, R., Rodrigues, P.P.: Issues in evaluation of stream learning algorithms. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’09, pp. 329–338. ACM, New York (2009)

  20. Gama, Ja, Sebastião, R., Rodrigues, P.P.: On evaluating stream learning algorithms. Mach. Learn. 90(3), 317–346 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  21. Gehrke, J., Ganti, V., Ramakrishnan, R., Loh, W.Y.: Boat—optimistic decision tree construction. In: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, SIGMOD ’99, pp. 169–180. ACM, New York (1999)

  22. Gibaja, E., Ventura, S.: Multi-label learning: a review of the state of the art and ongoing research. Wiley Interdiscip. Rev. Data Min. Knowl. Disc. 4(6), 411–444 (2014)

    Article  Google Scholar 

  23. Gibaja, E., Ventura, S.: A tutorial on multilabel learning. ACM Comput. Surv. 47(3), 1–38 (2015)

    Article  Google Scholar 

  24. Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inf. Retr. 4(2), 133–151 (2001)

    Article  MATH  Google Scholar 

  25. Haque, A., Khan, L., Baron, M., Thuraisingham, B., Aggarwal, C.: Efficient handling of concept drift and concept evolution over stream data. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 481–492 (2016)

  26. Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 1–19 (2015)

    Article  Google Scholar 

  27. Herrera, F., Charte, F., Rivera, A.J., del Jesus, M.J.: Multilabel classification. Multilabel Classification Problem Analysis, Metrics and Techniques, pp. 17–31. Springer International Publishing, Switzerland (2016)

  28. Hoeffding, W.: Probability inequalities for sums of bounded random variables. J. Am. Stat. Assoc. 58, 13–30 (1963)

    Article  MathSciNet  MATH  Google Scholar 

  29. Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’01, pp. 97–106. ACM, New York (2001)

  30. Jiang, X., Niu, Z., Guo, J., Mustafa, G., Lin, Z., Chen, B., Zhou, Q.: Novel boosting frameworks to improve the performance of collaborative filtering. In: Ong C.S., Ho T.B. (eds.) Proceedings of the 5th Asian Conference on Machine Learning, Proceedings of Machine Learning Research, vol. 29, pp. 87–99. PMLR, Australian National University, Canberra (2013)

  31. Kim, S., Kim, H., Namkoong, Y.: Ordinal classification of imbalanced data with application in emergency and disaster information services. IEEE Intell. Syst. 31(5), 50–56 (2016)

    Article  Google Scholar 

  32. Laghmari, K., Marsala, C., Ramdani, M.: Graded multi-label classification: compromise between handling label relations and limiting error propagation. In: 2016 11th International Conference on Intelligent Systems: Theories and Applications (SITA), pp. 1–6 (2016)

  33. Laghmari, K., Marsala, C., Ramdani, M.: Classification multi-labels graduée: Apprendre les relations entre les labels ou limiter la propagation d’erreur ? Revue des Nouvelles Technologies de l Information, Extraction et Gestion des Connaissances, RNTI-E-33, pp. 381–386 (2017)

  34. Lastra, G., Luaces, O., Bahamonde, A.: Interval prediction for graded multi-label classification. Pattern Recognit. Lett. 49, 171–176 (2014)

    Article  Google Scholar 

  35. Liu, C., Cao, L.: A Coupled k-Nearest Neighbor Algorithm for Multi-label Classification, pp. 176–187. Springer, Cham (2015)

    Google Scholar 

  36. Loeffel, P., Marsala, C., Detyniecki, M.: Classification with a reject option under concept drift: the droplets algorithm. In: 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015, Paris, 19–21 Oct 2015, pp. 1–9. IEEE (2015)

  37. Loeffel, P.X., Marsala, C., Detyniecki, M.: Memory management for data streams subject to concept drift. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 387–392 (2016)

  38. Loza Mencía, E., Janssen, F.: Learning rules for multi-label classification: a stacking and a separate-and-conquer approach. Mach. Learn. 105(1), 77–126 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  39. Marsala, C.: Incremental tuning of fuzzy decision trees. In: Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on, pp. 2061–2064 (2012)

  40. Mehta, M., Agrawal, R., Rissanen, J.: Sliq: A fast scalable classifier for data mining. In: Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology, EDBT ’96, pp. 18–32. Springer, London (1996)

  41. Nguyen, H.L., Woon, Y.K., Ng, W.K.: A survey on data stream clustering and classification. Knowl. Inf. Syst. 45(3), 535–569 (2015)

    Article  Google Scholar 

  42. Pan, R., Yang, T., Cao, J., Lu, K., Zhang, Z.: Missing data imputation by k nearest neighbours based on grey relational structure and mutual information. Appl. Intell. 43(3), 614–632 (2015)

    Article  Google Scholar 

  43. Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5), 393–408 (1999)

    Article  Google Scholar 

  44. Qiao, X.: Learning ordinal data. Wiley Interdiscip. Rev. Comput. Stat. 7(5), 341–346 (2015)

    Article  MathSciNet  Google Scholar 

  45. Qu, W., Zhang, Y., Zhu, J., Qiu, Q.: Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble. Springer, Berlin, Heidelberg (2009)

    Book  Google Scholar 

  46. Quinlan, J.: Learning efficient classification procedures and their application to chess end games. In: Mitchell, R.S.M.G.C.M. (ed.) Machine Learning, pp. 463–482. Morgan Kaufmann, San Francisco (1983)

    Google Scholar 

  47. Quinlan, J.: The minimum description length principle and categorical theories. In: Hirsh, W.W.C. (ed.) Machine Learning Proceedings 1994, pp. 233–241. Morgan Kaufmann, San Francisco (1994)

  48. Quinlan, J.R.: Induction of decision trees. Mach. Learn 1, 81–106 (1986)

    Google Scholar 

  49. Read, J., Bifet, A., Holmes, G., Pfahringer, B.: Scalable and efficient multi-label classification for evolving data streams. Mach. Learn. 88(1), 243–272 (2012)

    Article  MathSciNet  Google Scholar 

  50. Read, J., Martino, L., Olmos, P.M., Luengo, D.: Scalable multi-output label prediction: from classifier chains to classifier trellises. Pattern Recognit. 48(6), 2096–2109 (2015)

    Article  MATH  Google Scholar 

  51. Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333–359 (2011)

  52. Salperwyck, C., Lemaire, V.: Incremental decision tree based on order statistics. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2013)

  53. Schlimmer, J.C., Fisher, D.: A case study of incremental concept induction. In: Proceedings of the Fifth National Conference on Artificial Intelligence, pp. 496–501 (1986)

  54. Shafer, J.C., Agrawal, R., Mehta, M.: Sprint: A scalable parallel classifier for data mining. In: Proceedings of the 22th International Conference on Very Large Data Bases, VLDB ’96, pp. 544–555. Morgan Kaufmann Publishers Inc., San Francisco (1996)

  55. Sun, Z., Guo, Z., Jiang, M., Wang, X., Liu, C.: Research and Application of Fast Multi-label SVM Classification Algorithm Using Approximate Extreme Points. Springer, Cham (2016)

    Book  Google Scholar 

  56. Utgoff, P.E.: Id5: an incremental id3. In: Laird, J.E. (ed.) ML, pp. 107–120. Morgan Kaufmann, San Francisco (1988)

    Google Scholar 

  57. Utgoff, P.E.: Incremental induction of decision trees. Mach. Learn. 4(2), 161–186 (1989)

    Article  Google Scholar 

  58. Utgoff, P.E.: An improved algorithm for incremental induction of decision trees. In: In Proceedings of the Eleventh International Conference on Machine Learning, pp. 318–325. Morgan Kaufmann (1994)

  59. Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’03, pp. 226–235. ACM, New York (2003)

  60. Wang, X., An, S., Shi, H., Hu, Q.: Fuzzy Rough Decision Trees for Multi-label Classification. Springer, Cham (2015)

    Book  Google Scholar 

  61. Wu, Q., Tan, M., Song, H., Chen, J., Ng, M.K.: Ml-forest: a multi-label tree ensemble method for multi-label classification. IEEE Trans. Knowl. Data Eng. 28(10), 2665–2680 (2016)

    Article  Google Scholar 

  62. Xie, W., Ouyang, Y., Ouyang, J., Rong, W., Xiong, Z.: User occupation aware conditional restricted boltzmann machine based recommendation. In: 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 454–461 (2016)

  63. Xioufis, E.S., Spiliopoulou, M., Tsoumakas, G., Vlahavas, I.: Dealing with concept drift and class imbalance in multi-label stream classification. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI’11, Vol. Volume Two, pp. 1583–1588. AAAI Press (2011)

  64. Yang, X., Guo, Y., Liu, Y., Steck, H.: A survey of collaborative filtering based social recommender systems. Comput. Commun. 41, 1–10 (2014)

    Article  Google Scholar 

  65. Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  66. Zaier, Z., Godin, R., Faucher, L.: Evaluating recommender systems. In: 2008 International Conference on Automated Solutions for Cross Media Content and Multi-Channel Distribution, pp. 211–217 (2008)

  67. Zaier, Z., Godin, R., Faucher, L.: Recommendation quality evolution based on neighbors discrimination. In: 2008 International MCETECH Conference on e-Technologies (mcetech 2008), pp. 148–153 (2008)

  68. Žliobaitė, I., Pechenizkiy, M., Gama, J.: An Overview of Concept Drift Applications. Springer, Cham (2016)

    MATH  Google Scholar 

Download references

Acknowledgements

This work has been partially funded by the Ministère de l’Enseignement Supérieur, de la Recherche Scientifique et de la Formation des Cadres (MESRSFC) of Morocco and the French Institute of the French Embassy in Morocco.

Author information

Authors and Affiliations

  1. Laboratoire Informatique de Mohammedia, FSTM Hassan II University of Casablanca, BP 146, 20650, Mohammedia, Morocco

    Khalil Laghmari & Mohammed Ramdani

  2. Sorbonne Universités, UPMC Univ Paris 06, CNRS, LIP6 UMR 7606, 4 place Jussieu, Paris, 75005, France

    Khalil Laghmari & Christophe Marsala

Authors
  1. Khalil Laghmari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christophe Marsala
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammed Ramdani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khalil Laghmari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Laghmari, K., Marsala, C. & Ramdani, M. An adapted incremental graded multi-label classification model for recommendation systems. Prog Artif Intell 7, 15–29 (2018). https://doi.org/10.1007/s13748-017-0133-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13748-017-0133-5

Keywords

Search

Navigation