Advertisement

A Rule Based Recommender System

  • Bruno Apolloni
  • Simone BassisEmail author
  • Marco Mesiti
  • Stefano Valtolina
  • Francesco Epifania
Conference paper
  • 1.2k Downloads
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 54)

Abstract

We introduce a new recommending paradigm based on the genomic features of the candidate objects. The system is based on the tree structure of the object metadata which we convert in acceptance rules, leaving the user the discretion of selecting the most convincing rules for her/his scope. We framed the deriving recommendation system on a content management platform within the scope of the European Project NETT and tested it on the Entree UCI benchmark.

Keywords

Recommender system Decision trees Genomic features 

Notes

Acknowledgments

This work has been supported by the European Project NETT.

References

  1. 1.
    Balabanovic, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40, 66–72 (1997)CrossRefGoogle Scholar
  2. 2.
    Billsus, D., Pazzani, M.: Learning collaborative information filters. In: 15th International Conference on Machine Learning, pp. 46–54. Morgan Kaufmann (1998)Google Scholar
  3. 3.
    Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: 14th Conference on Uncertainty in Artificial Intelligence (1998)Google Scholar
  4. 4.
    Burke, R.: The Wasabi personal shopper: a case-based recommender system. In: 11th National Conference on Innovative Applications of Artificial Intelligence, pp. 844–849 (1999)Google Scholar
  5. 5.
    Cohen, W.W.: Fast effective rule induction. In: Twelfth International Conference on Machine Learning, pp. 115–123. Morgan Kaufmann (1995)Google Scholar
  6. 6.
    Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Shavlik, J. (ed.) 15th International Conference on Machine Learning, pp. 144–151. Morgan Kaufmann (1998)Google Scholar
  7. 7.
    Guttman, R.H., Moukas, A.G., Maes, P.: Agent-mediated electronic commerce: a survey. Knowl. Eng. Rev. 13(2), 147–159 (1998)CrossRefGoogle Scholar
  8. 8.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1) (2009). http://www.cs.waikato.ac.nz/ml/weka Google Scholar
  9. 9.
    Hettich, S., Bay, S.D.: The UCI KDD Archive (1999). http://kdd.ics.uci.edu
  10. 10.
    Hofmann, T.: Latent semantic analysis for collaborative filtering. AACM Trans. Inf. Syst. (2004)Google Scholar
  11. 11.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42, 30–37 (2009)Google Scholar
  12. 12.
    Krulwich, B.: Lifestyle finder: intelligent user profiling using large-scale demographic data. Artif. Intell. Mag. 18(2), 37–45 (1997)Google Scholar
  13. 13.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 788, 401 (1999)Google Scholar
  14. 14.
    Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: 18th National Conference on Artificial Intelligence, pp. 187–192 (2002)Google Scholar
  15. 15.
    Montaner, M., López, B., De La Rosa, J.L.: A taxonomy of recommender agents on the Internet. Artif. Intell. Rev. 19(4), 285–330 (2003)CrossRefGoogle Scholar
  16. 16.
    Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: 5th ACM Conference on Digital Libraries, pp. 187–192 (2000)Google Scholar
  17. 17.
    Pan, R., Scholz, M.: Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering. In: 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2009)Google Scholar
  18. 18.
    Pazzani, M.J., Billsus, D.: The identification of interesting web sites. Mach. Learn. 27, 313–331 (1997)CrossRefGoogle Scholar
  19. 19.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1993)Google Scholar
  20. 20.
    Schein, A., Popescul, A., Ungar, L., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 253–260. ACM (2002)Google Scholar
  21. 21.
    Shalon, D., Smith, S., Brown, P.: A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Res. 6(7), 639–645 (1996)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Bruno Apolloni
    • 1
  • Simone Bassis
    • 1
    Email author
  • Marco Mesiti
    • 1
  • Stefano Valtolina
    • 1
  • Francesco Epifania
    • 1
    • 2
  1. 1.Dipartimento di InformaticaUniversità degli Studi di MilanoMilanItaly
  2. 2.Social Things S.r.l.MilanoItaly

Personalised recommendations