Skip to main content

A Case-Based Solution to the Cold-Start Problem in Group Recommenders

  • Conference paper
Case-Based Reasoning Research and Development (ICCBR 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7466))

Included in the following conference series:

Abstract

We extend a group recommender system with a case base of previous group recommendation events. We show that this offers a potential solution to the cold-start problem. Suppose a group recommendation is sought but one of the group members is a new user who has few item ratings. We can copy ratings into this user’s profile from the profile of the most similar user in the most similar group from the case base. In other words, we copy ratings from a user who played a similar role in some previous group event. We show that copying in this way, i.e. conditioned on groups, is superior to copying nothing and also superior to copying ratings from the most similar user known to the system.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

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. Balabanovic, M., Shoham, Y.: Fab: Content-based, collaborative recommendation. Comms. of the Association of Computing Machinery 40(3), 66–72 (1997)

    Article  Google Scholar 

  2. Golbandi, N., Koren, Y., Lempel, R.: Adaptive bootstrapping of recommender systems using decision trees. In: Fourth ACM International Conference on Web Search and Data Mining (2011)

    Google Scholar 

  3. Herlocker, J.L.: Understanding and Improving Automated Collaborative Filtering Systems. PhD thesis. University of Minnesota (2000)

    Google Scholar 

  4. Jameson, A., Smyth, B.: Recommendation to Groups. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 596–627. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Procs. of the Eighteenth National Conference on Artificial Intelligence (AAAI), pp. 187–192 (2002)

    Google Scholar 

  7. Sullivan, D.O., Wilson, D.C., Smyth, B.: Improving Case-Based Recommendation: A Collaborative Filtering Approach. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 278–291. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  8. Quijano-Sánchez, L., Recio-García, J.A., Díaz-Agudo, B.: An architecture for developing group recommender systems enhanced by social elements. International Journal of Human-Computer Studies (in press, 2012)

    Google Scholar 

  9. Quijano-Sánchez, L., Recio-García, J.A., Díaz-Agudo, B., Jiménez-Díaz, G.: Happy movie: A group recommender application in facebook. In: 24th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2011 (2011)

    Google Scholar 

  10. Quijano-Sánchez, L., Recio-García, J.A., Díaz-Agudo, B., Jiménez-Díaz, G.: Social factors in group recommender systems. In: ACM-TIST, TIST-2011-01-0013 (in press, 2011)

    Google Scholar 

  11. Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., Riedl, J.: Getting to know you: Learning new user preferences in recommender systems. In: Proceedings of the 2002 International Conference on Intelligent User Interfaces, pp. 127–134 (2002)

    Google Scholar 

  12. Ricci, F., Arslan, B., Mirzadeh, N., Venturini, A.: ITR: A Case-Based Travel Advisory System. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 613–641. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  13. Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative Filtering Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  14. Schaubhut, N.A.: Technical Brief For The Thomas-Kilmann Conflict Mode Instrument. CPP Research Department (2007)

    Google Scholar 

  15. Thomas., K.W., Kilmann, R.H.: Thomas-Kilmann Conflict Mode Instrument, Tuxedo, N.Y. (1974)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Quijano-Sánchez, L., Bridge, D., Díaz-Agudo, B., Recio-García, J.A. (2012). A Case-Based Solution to the Cold-Start Problem in Group Recommenders. In: Agudo, B.D., Watson, I. (eds) Case-Based Reasoning Research and Development. ICCBR 2012. Lecture Notes in Computer Science(), vol 7466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32986-9_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32986-9_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32985-2

  • Online ISBN: 978-3-642-32986-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics