Skip to main content
SpringerLink
Log in

How to Get the Recommender Out of the Lab?

  • Chapter
  • First Online:
Book cover Recommender Systems Handbook

Abstract

A personalised system is a complex system made of many interacting parts, from data ingestion to presenting the results to the users. A plethora of methods, tools, algorithms and approaches exist for each piece of such a system: many data and metadata processing methods, many user models, many filtering techniques, many accuracy metrics, many personalisation levels. In addition, a realworld recommender is a piece of an even larger and more complex environment on which there is little control: often the recommender is part of a larger application introducing constraints for the design of the recommender, e.g. the data may not be in a suitable format, or the environment may impose some architectural or privacy constraints. This can make the task of building such a recommender system daunting, and it is easy to make errors. Based on the experience of the authors and the study of other works, this chapter intends to be a guide on the design, implementation and evaluation of personalised systems. It presents the different aspects that must be studied before the design is even started, and how to avoid pitfalls, in a hands-on approach. The chapter presents the main factors to take into account to design a recommender system, and illustrates them through case studies of existing systems to help navigate in the many and complex choices that have to be faced.

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, E.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)

    Article  Google Scholar 

  2. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, vol. 22, pp. 207–216. ACM (1993)

    Google Scholar 

  3. Ali, K., van Stam, W.: Tivo: making show recommendations using a distributed collaborative filtering architecture. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. Seattle, WA, USA (2004)

    Google Scholar 

  4. Anand, S.S., Mobasher, B.: Contextual recommendation. In: From Web to Social Web: Discovering and Deploying User and Content Profiles, Lecture Notes in Computer Science, pp. 142–160. Springer-Verlag (2007)

    Google Scholar 

  5. Anderson, C.: The long tail. Wired (2004)

    Google Scholar 

  6. Ardissono, L., Goy, A., Petrone, G., Segnan, M., Torasso, P.: Intrigue: Personalized recommendation of tourist attractions for desktop and handset devices. In: Applied Artificial Intelligence, p. 687–714. Taylor and Francis (2003)

    Google Scholar 

  7. Balabanovi´c, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Communications of the ACM 40(3), 66–72 (1997)

    Article  Google Scholar 

  8. Bernhaupt, R., Wilfinger, D., Weiss, A., Tscheligi, M.: An ethnographic study on recommendations in the living room: Implications for the design of itv recommender systems. In: EUROITV’ 08: Proceedings of the 6th European conference on Changing Television Environments, pp. 92–101. Springer-Verlag (2008)

    Google Scholar 

  9. Bias, R., Mayhew, D.: Cost-Justifying usability. Morgan Kaufman Publishing (1994)

    Google Scholar 

  10. Bolger, N., Davis, A., Rafaeli, E.: Diary methods: Capturing life as it is lived. In Annual Review of Psychology 54(1), 579–616 (2003)

    Article  Google Scholar 

  11. Bonnefoy, D., Bouzid, M., Lhuillier, N., Mercer, K.: More like this or not for me: Delivering personalised recommendations in multi-user environments. In: UM’07: Proceedings of the 11th international conference on User Modeling, pp. 87–96. Springer-Verlag (2007)

    Google Scholar 

  12. Bonnefoy, D., Drgehorn, O., Kernchen, R.: Enabling Technologies for Mobile Services: The Mobilife Book, chap. Multimodality and Personalisation. John Wiley & Sons Ltd (2007)

    Google Scholar 

  13. Bonnefoy, D., Picault, J., Bouzid, M.: Distributed user profile. Patent applications EP1934901, GB2430281 & WO2007037870 (2005)

    Google Scholar 

  14. Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)

    Google Scholar 

  15. Cantador, I.: Exploiting the conceptual space in hybrid recommender systems: a semanticbased approach. Ph.D. thesis, Universidad Autnoma de Madrid (UAM), Spain (2008)

    Google Scholar 

  16. Cantador, I., Bellogn, A., Castells, P.: News@hand: A semantic web approach to recommending news. In: Proceedings of the 5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH 2008), Lecture Notes in Computer Science, vol. 5149, pp. 279–283. Springer-Verlag (2008)

    Google Scholar 

  17. Cantador, I., Fernández, M., Vallet, D., Castells, P., Picault, J., Ribière, M.: Advances in Semantic Media Adaptation and Personalization, Studies in Computational Intelligence, vol. 93, chap. A Multi-Purpose Ontology-Based Approach for Personalised Content Filtering and Retrieval, pp. 25–51. Springer (2008)

    Google Scholar 

  18. Cantador, I., Szomszor, M., Alani, H., Fernández, M., Castells, P.: Enriching ontological user profiles with tagging history for multi-domain recommendations. In: Proceedings of the 1st International Workshop on Collective Semantics: Collective Intelligence and the Semantic Web (CISWeb 2008), pp. 5–19 (2008)

    Google Scholar 

  19. Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: Gate: A framework and graphical development environment for robust nlp tools and applications. In: Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics (ACL’02) (2002)

    Google Scholar 

  20. Dong, J., Martin, S., Waldo, P.: A conference on human factors in computing systemser input and analysis tool for information architecture. In: Conference on Human Factors in Computing Systems, pp. 23–24 (2001)

    Google Scholar 

  21. Duda, R.O., Hart, P., Stork, D.G.: Pattern Classification. John Wiley, New York (2001)

    MATH  Google Scholar 

  22. Gilb, T.: Principles of software engineering management. Arron Marcus Associates (1988)

    Google Scholar 

  23. Ha, S.H.: An intelligent system for personalized advertising on the internet. LNCS 3182 pp. 21–30 (2004) 364 J´erome Picault, Myriam Ribière, David Bonnefoy and Kevin Mercer

    Google Scholar 

  24. Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: CSCW’00: Proceedings of the 2000 ACM conference on Computer supported cooperative work, pp. 241–250. ACM, New York, NY, USA (2000)

    Chapter  Google Scholar 

  25. Herlocker, J.L., Terveen, L.G., Konstan, J.A., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on information systems 22, 5–53 (2004)

    Article  Google Scholar 

  26. International Organisation for Standardisation (ISO): ISO 13407: Human centred design processes for interactive systems.

    Google Scholar 

  27. Jameson, A., Baldes, S., Kleinbauer, T.: Two methods for enhancing mutual awareness in a group recommender system. In: AVI’04: Proceedings of the working conference on Advanced visual interfaces, pp. 447–449. ACM, New York, NY, USA (2004)

    Chapter  Google Scholar 

  28. Lhuillier, N., Bonnefoy, D., Bouzid, M., Millerat, J., Picault, J., Ribière, M.: A recommendation system and method of operation therefor. Patent application WO2008073595 (2006)

    Google Scholar 

  29. Lhuillier, N., Bouzid, M., Gadanho, S.: Context-sensitive user preference prediction. Patent application GB2442024 (2006)

    Google Scholar 

  30. Lhuillier, N., Bouzid, M., Mercer, K., Picault, J.: System for content item recommendation. Patent application GB2438645 (2006)

    Google Scholar 

  31. Masthoff, J.: Group modeling: Selecting a sequence of television items to suit a group of viewers. User Modeling and User-Adapted Interaction 14(1), 37–85 (2004)

    Google Scholar 

  32. McCarthy, K., Salamó, M., Coyle, L., McGinty, L., Smyth, B., Nixon, P.: Cats: A synchronous approach to collaborative group recommendation. In: G. Sutcliffe, R. Goebel (eds.) Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference, pp. 86–91. AAAI Press (2006)

    Google Scholar 

  33. McNee, S.M., Riedl, J., Konstan, J.A.: Accurate is not always good: How accuracy metrics have hurt recommender systems. In: CHI’06 extended abstracts on Human factors in computing systems, pp. 1097–1101. ACM, New York, NY, USA (2006)

    Chapter  Google Scholar 

  34. Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. Internet Technol. 7(4) (2007)

    Google Scholar 

  35. Mobasher, B., Jin, X., Zhou, Y.: Semantically enhanced collaborative filtering on the web. Web Mining: FromWeb to SemanticWeb pp. 57–76 (2004)

    Google Scholar 

  36. Oppenheim, A.: Questionnaire Design, Interviewing and Attitude Measurement. Continuum International Publishing, New York (2001)

    Google Scholar 

  37. Papadogiorgaki, M., Papastathis, V., Nidelkou, E., Kompatsiaris, Y., Waddington, S., Bratu, B., Ribière, M.: Distributed user modeling for personalized news delivery in mobile devices. In: 2nd International Workshop on Semantic Media Adaptation and Personalization (2007)

    Google Scholar 

  38. icault, J., Ribière, M.: An empirical user profile adaptation mechanism that reacts to shifts of interests. Submitted to the 18th European Conference on Artificial Intelligence (2008). http://www.mesh-ip.eu/upload/ecai2008.pdf

  39. Picault, J., Ribière, M.: Method of adapting a user profile including user preferences and communication device. European Patent EP08290033 (2008)

    Google Scholar 

  40. Ribière, M., Picault, J.: Progressive display of user interests. Tech. rep., Motorola (2008). https://priorart.ip.com/download/IPCOM000167391D/

    Google Scholar 

  41. Ribière, M., Picault, J.: Method and apparatus for modifying a user preference profile. Patent application WO2009064585 (2009)

    Google Scholar 

  42. Rich, E.M.: User modeling via stereotypes. Cognitive Science 3, 329–354 (1979)

    Article  Google Scholar 

  43. Rokach, L. and Maimon, O. and Arbel, R., Selective voting-getting more for less in sensor fusion, International Journal of Pattern Recognition and Artificial Intelligence 20(3):329–350 (2006)

    Article  Google Scholar 

  44. Rugg, G., McGeorge, P.: The sorting techniques: a tutorial paper on card sorts, picture sorts and item sorts. Expert Systems, The journal of Knowledge Engineering 14(2), 80–93 (2002)

    Google Scholar 

  45. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW’01: Proceedings of the 10th international conference on World Wide Web, pp. 285–295. ACM, New York, NY, USA (2001)

    Google Scholar 

  46. Shepherd, M.: Tutorial on personalization and filtering on the web. Web Information Filtering Lab, Dalhousie University, Canada. http://ncsinet.ncsi.iisc.ernet.in/gsdl/collect/icco/index/assoc/HASH011b.dir/Tutorial-Shepherd.ppt

  47. Shoval, P., Maidel, V., Shapira, B.: An ontology-content-based filtering method. International Journal of Information Theories and Applications (15), 303–318 (2008)

    Google Scholar 

  48. Sieg, A., Mobasher, B., Burke, R.: Ontological user profiles for personalized web search. In: Proceedings of AAAI 2007 Workshop on Intelligent Techniques for Web Personalization, pp. 84–91. Vancouver, BC, Canada (2007)

    Google Scholar 

  49. Signa, R.: Design strategies for recommender systems. UIE Web App Summit. http://www.slideshare.net/rashmi/design-of-recommender-systems

  50. Snyder, C.: Paper prototyping: The fast and east way to design and refine user interfaces. Morgan Kaufmann Publishing, San Francisco (2003)

    Google Scholar 

  51. Terveen, L., Hill, W.: Human-Computer Interaction in the New Millennium, chap. Beyond Recommender Systems: Helping People Help Each Other, pp. 487–509

    Google Scholar 

  52. Villegas, P., Sarris, N., Picault, J., Kompatsiaris, I.: Creating a mesh of multimedia news feeds. In: European Workshop on the Integration of Knowledge, Semantics and Digital Media Technologies (EWIMT), pp. 453–454. IEE (2005)

    Google Scholar 

  53. Yu, Z., Zhou, X., Hao, Y., Gu, J.: Tv program recommendation for multiple viewers based on user profile merging. User Modeling and User-Adapted Interaction 16(1), 63–82 (2006)

    Google Scholar 

  54. Zhang, M., Hurley, N.: Avoiding monotony: improving the diversity of recommendation lists. In: RecSys’08: Proceedings of the 2008 ACM conference on Recommender systems, pp. 123–130. ACM, New York, NY, USA (2008)

    Chapter  Google Scholar 

  55. Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: WWW’05: Proceedings of the 14th international conference on World Wide Web, pp. 22–32. ACM, New York (2005)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Alcatel-Lucent Bell Labs, New Providence, New Jersey, USA

    Jérome Picault & Myriam Ribière

  2. Pearltrees, Paris, France

    David Bonnefoy

  3. Loughborough University, Loughborough, UK

    Kevin Mercer

Authors
  1. Jérome Picault
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Myriam Ribière
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Bonnefoy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kevin Mercer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jérome Picault .

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

Picault, J., Ribière, M., Bonnefoy, D., Mercer, K. (2011). How to Get the Recommender Out of the Lab?. 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_10

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-85820-3_10

  • 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