Skip to main content

A Rule Based Recommender System

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

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-33747-0_9
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   229.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-33747-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   299.99
Price excludes VAT (USA)
Hardcover Book
USD   299.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3

Notes

  1. 1.

    http://siren.laren.di.unimi.it/nett/mnett/.

References

  1. Balabanovic, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40, 66–72 (1997)

    CrossRef  Google Scholar 

  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. 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. 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. Cohen, W.W.: Fast effective rule induction. In: Twelfth International Conference on Machine Learning, pp. 115–123. Morgan Kaufmann (1995)

    Google Scholar 

  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. Guttman, R.H., Moukas, A.G., Maes, P.: Agent-mediated electronic commerce: a survey. Knowl. Eng. Rev. 13(2), 147–159 (1998)

    CrossRef  Google Scholar 

  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. Hettich, S., Bay, S.D.: The UCI KDD Archive (1999). http://kdd.ics.uci.edu

  10. Hofmann, T.: Latent semantic analysis for collaborative filtering. AACM Trans. Inf. Syst. (2004)

    Google Scholar 

  11. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42, 30–37 (2009)

    Google Scholar 

  12. Krulwich, B.: Lifestyle finder: intelligent user profiling using large-scale demographic data. Artif. Intell. Mag. 18(2), 37–45 (1997)

    Google Scholar 

  13. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 788, 401 (1999)

    Google Scholar 

  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. 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)

    CrossRef  Google Scholar 

  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. 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. Pazzani, M.J., Billsus, D.: The identification of interesting web sites. Mach. Learn. 27, 313–331 (1997)

    CrossRef  Google Scholar 

  19. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1993)

    Google Scholar 

  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. 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)

    CrossRef  Google Scholar 

Download references

Acknowledgments

This work has been supported by the European Project NETT.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Simone Bassis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Apolloni, B., Bassis, S., Mesiti, M., Valtolina, S., Epifania, F. (2016). A Rule Based Recommender System. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-33747-0_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33746-3

  • Online ISBN: 978-3-319-33747-0

  • eBook Packages: EngineeringEngineering (R0)