Skip to main content
SpringerLink
Log in
SpringerLink
Log in

A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment

  • Chapter
  • First Online:
Recommender Systems Handbook

Abstract

In this chapter we describe the integration of a recommender system into the production environment of Fastweb, one of the largest European IP Television (IPTV) providers. The recommender system implements both collaborative and content-based techniques, suitable tailored to the specific requirements of an IPTV architecture, such as the limited screen definition, the reduced navigation capabilities, and the strict time constraints. The algorithms are extensively analyzed by means of off-line and on-line tests, showing the effectiveness of the recommender systems: up to 30% of the recommendations are followed by a purchase, with an estimated lift factor (increase in sales) of 15%.

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

Access this chapter

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 179.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on 17(6), 734–749 (2005). DOI 10.1109/TKDE.2005.99

    Google Scholar 

  2. Anderson, C.: The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion (2006). URL http://www.amazon.ca/exec/obidos/redirect?tag=citeulike04-20{\&}path=ASIN/1401302378

  3. Balabanovi´c, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997). DOI http://doi.acm.org/10.1145/245108.245124

    Google Scholar 

  4. Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. 7th IEEE Int. Conf. on Data Mining pp. 43–52 (2007)

    Google Scholar 

  5. Berry, M.W.: Large-scale sparse singular value computations. The International Journal of Supercomputer Applications 6(1), 13–49 (1992). URL citeseer.ist.psu.edu/berry92large.html

    Google Scholar 

  6. Chai, K.M.A., Chieu, H.L., Ng, H.T.: Bayesian online classifiers for text classification and filtering pp. 97–104 (2002). DOI http://doi.acm.org/10.1145/564376.564395

  7. Cremonesi, P., Lentini, E., Matteucci, M., Turrin, R.: An evaluation methodology for recommender systems. 4th Int. Conf. on Automated Solutions for Cross Media Content and Multi-channel Distribution pp. 224–231 (2008)

    Google Scholar 

  8. Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. Journal of the American Society of Information Science 41(6), 391– 407 (1990). URL http://citeseer.ist.psu.edu/deerwester90indexing.html

    Google Scholar 

  9. Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS) 22(1), 143–177 (2004). DOI http://doi.acm.org/10.1145/963770.963776

    Google Scholar 

  10. Furnas, G.W., Deerwester, S., Dumais, S.T., Landauer, T.K., Harshman, R.A., Streeter, L.A., Lochbaum, K.E.: Information retrieval using a singular value decomposition model of latent semantic structure. pp. 465–480. ACM Press, New York, NY, USA (1988). DOI http://doi.acm.org/10.1145/62437.62487

  11. Geneve, U.D., Marchand-maillet, S.: Vision content-based video retrieval: An overview 330 Riccardo Bambini, Paolo Cremonesi and Roberto Turrin

    Google Scholar 

  12. Gorrell, G.: Generalized Hebbian Algorithm for Incremental Singular Value Decomposition in Natural Language Processing. 11th Conference of the European Chapter of the Association for Compuational Linguistics (2006)

    Google Scholar 

  13. Hand, S., Varan, D.: Interactive narratives: Exploring the links between empathy, interactivity and structure pp. 11–19 (2008)

    Google Scholar 

  14. Herlocker, J., Konstan, J., Riedl, J.: An algorithmic framework for performing collaborative filtering. 22nd ACM SIGIR Conf. on R&D in Information Retrieval pp. 230–237 (1999)

    Google Scholar 

  15. Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) 22(1), 5–53 (2004)

    Article  Google Scholar 

  16. Husbands, P., Simon, H., Ding, C.: On the use of singular value decomposition for text retrieval (2000). URL citeseer.ist.psu.edu/article/husbands00use.html

    Google Scholar 

  17. Jensen, J.F.: Interactive television - a brief media history 5066, 1–10 (2008)

    Google Scholar 

  18. Kelly, D., Teevan, J.: Implicit feedback for inferring user preference: a bibliography. SIGIR Forum 37(2), 18–28 (2003). DOI http://doi.acm.org/10.1145/959258.959260

    Google Scholar 

  19. Lee, Y., Lee, J., Kim, I., Shin, H.: Reducing iptv channel switching time using h.264 scalable video coding. Consumer Electronics, IEEE Transactions on 54(2), 912–919 (2008). DOI 10.1109/TCE.2008.4560178

    Google Scholar 

  20. Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. Proceedings of KDD Cup and Workshop (2007)

    Google Scholar 

  21. Pazzani, M., Billsus, D.: Content-based recommendation systems. The AdaptiveWeb: Methods and Strategies of Web Personalization, Lecture Notes in Computer Science pp. 325–341 (2006)

    Google Scholar 

  22. Powers, D.M.W.: Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness and Correlation (2000)

    Google Scholar 

  23. Rafey, R.A., Gibbs, S., Hoch, M., Gong, H.L.V., Wang, S.: Enabling custom enhancements in digital sports broadcasts pp. 101–107 (2001). DOI http://doi.acm.org/10.1145/363361.63384

  24. Rokach, L., Maimon, O., Averbuch, M., Information Retrieval System for Medical Narrative Reports, Lecture Notes in Artificial intelligence 3055, page 217-228 Springer-Verlag (2004)

    Google Scholar 

  25. Saad, Y.: Numerical methods for large eigenvalue problems. Halsted Press New York (1992)

    MATH  Google Scholar 

  26. Salton, G. (ed.): Automatic text processing. Addison-Wesley Longman Publishing Co., Inc.,lBoston, MA, USA (1988)

    Google Scholar 

  27. Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. 10th Int. Conf. on World Wide Web pp. 285–295 (2001)

    Google Scholar 

  28. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of Dimensionality Reduction in Recommender System-A Case Study. Defense Technical Information Center (2000)

    Google Scholar 

  29. Schafer, J., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. pp. 291–324 (2007)

    Google Scholar 

  30. Sun, J., Gao, S.: Iptv based on ip network and streaming media service station. MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications 6790(1), 67904Q (2007). DOI 10.1117/12.749611. URL http://link.aip.org/link/?PSI/6790/67904Q/1

  31. Valle-Lisboa, J.C., Mizraji, E.: The uncovering of hidden structures by latent semantic analysis. Inf. Sci. 177(19), 4122–4147 (2007). DOI http://dx.doi.org/10.1016/j.ins.2007.04.007

    Google Scholar 

  32. Van Rijsbergen, C.J.: Information Retrieval, 2nd edition. Dept. of Computer Science, University of Glasgow (1979). URL citeseer.ist.psu.edu/vanrijsbergen79information.html

    Google Scholar 

  33. Vozalis, E., Margaritis, K.: Analysis of recommender systems algorithms. Proc. of the 6th Hellenic European Conf. on Computer Mathematics and its Applications (2003)

    Google Scholar 

  34. Wang, J., de Vries, A.P., Reinders, M.J.T.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. pp. 501–508. ACM Press, New York, NY, USA (2006). DOI http://doi.acm.org/10.1145/1148170.1148257 9 A Recommender System for an IPTV Service Provider 331

  35. Zhang, H., Zheng, S., Yuan, J.: A personalized tv guide system compliant with mhp. Consumer Electronics, IEEE Transactions on 51(2), 731–737 (2005). DOI 10.1109/TCE.2005.1468026

    Google Scholar 

  36. Zhang, X., Berry, M.W., Raghavan, P.: Level search schemes for information filtering and retrieval. Information Processing and Management 37(2), 313–334 (2001). DOI http://dx.doi.org/10.1016/S0306-4573(00)00032-7

Download references

Author information

Authors and Affiliations

  1. Fastweb, via Francesco Caracciolo 51, Milano, Italy

    Riccardo Bambini

  2. Politecnico di Milano, p.zza Leonardo da Vinci 32, Milano, Italy

    Paolo Cremonesi & Roberto Turrin

  3. Neptuny, via Durando 10, Milano, Italy

    Paolo Cremonesi & Roberto Turrin

Authors
  1. Riccardo Bambini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paolo Cremonesi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roberto Turrin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Riccardo Bambini .

Editor information

Editors and Affiliations

  1. , Faculty of Computer Science, Free University of Bozen-Bolzano, Piazza Domenicani 3, Bolzano, 39100, Italy

    Francesco Ricci

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

  3. Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

  4. School of Communication,, Information & Library Studies, Rutgers University, Huntington Street 4, New Brunswick, 08901-1071, New Jersey, USA

    Paul B. Kantor

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Bambini, R., Cremonesi, P., Turrin, R. (2011). A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85820-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-85820-3_9

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-85819-7

  • Online ISBN: 978-0-387-85820-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation