Skip to main content
SpringerLink
Log in

Comparison-Based Interactive Collaborative Filtering

  • Conference paper
  • First Online:
Structural Information and Communication Complexity (SIROCCO 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9439))

  • 667 Accesses

Abstract

We study the interactive model of comparison-based collaborative filtering. Each player prefers one object from each pair of objects. However, revealing what is a player preference between two objects can be done only by asking the player specifically about that pair, an action called probing. The goal is to (approximately) reconstruct the players’ preferences with the smallest possible number of probes per player. The per-player number of probes can be reduced if there are many players who share a similar taste, but a priori, players do not know who to collaborate with. In this paper, we present the model of comparison-based interactive collaborative filtering, analyze a few possible taste models and present distributed algorithms whose output is close to the best possible approximation to the players’ taste.

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 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. Ailon, N.: Active learning ranking from pairwise preferences with almost optimal query complexity. In: Proc. NIPS, pp. 810–818 (2011)

    Google Scholar 

  2. Ailon, N., Charikar, M., Newman, A.: Aggregating inconsistent information: ranking and clustering. J. ACM 55(5), 23 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  3. Alon, N.: Ranking tournaments. SIAM Journal on Discrete Mathematics 20(1), 137–142 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  4. Alon, N., Awerbuch, B., Azar, Y., Patt-Shamir, B.: Tell me who I am: an interactive recommendation system. Theory of Computing Systems 45(2), 261–279 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  5. Awerbuch, B., Azar, Y., Lotker, Z., Patt-Shamir, B., Tuttle, M.R.: Collaborate with strangers to find own preferences. Theory of Computing Systems 42(1), 27–41 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  6. Azar, Y., Nisgav, A., Patt-Shamir, B.: Recommender systems with non-binary grades. In: Proc. 23rd SPAA, pp. 245–252. ACM (2011)

    Google Scholar 

  7. Bell, R.M., Koren, Y.: Lessons from the netflix prize challenge. SIGKDD Explorations 9(2), 75–79 (2007)

    Article  Google Scholar 

  8. Carterette, B., Bennett, P.N., Chickering, D.M., Dumais, S.T.: Here or there. In: Proc. 30th European Conf. on Advances in Information Retrieval, pp. 16–27 (2008)

    Google Scholar 

  9. Desarkar, M.S., Saxena, R., Sarkar, S.: Preference relation based matrix factorization for recommender systems. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 63–75. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  10. Drineas, P., Kerenidis, I., Raghavan, P.: Competitive recommendation systems. In: Proc. 34th Ann. ACM Symp. on Theory of Computing, pp. 82–90. ACM (2002)

    Google Scholar 

  11. Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval 4(2), 133–151 (2001)

    Article  MATH  Google Scholar 

  12. Jones, N., Brun, A., Boyer, A.: Comparisons instead of ratings: Towards more stable preferences. In: Proc. Int. Conf. on Web Intelligence and Intelligent Agent Technology, pp. 451–456. IEEE Computer Society (2011)

    Google Scholar 

  13. Kenyon-Mathieu, C., Schudy, W.: How to rank with few errors. In: Proc. 39th Ann. ACM Symp. on Theory of Computing, pp. 95–103. ACM (2007)

    Google Scholar 

  14. Loepp, B., Hussein, T., Ziegler, J.: Choice-based preference elicitation for collaborative filtering recommender systems. In: Proc. 32nd Ann. ACM Conf. on Human Factors in Computing Systems, pp. 3085–3094 (2014)

    Google Scholar 

  15. Nisgav, A., Patt-Shamir, B.: Improved collaborative filtering. In: Asano, T., Nakano, S.-i., Okamoto, Y., Watanabe, O. (eds.) ISAAC 2011. LNCS, vol. 7074, pp. 425–434. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  16. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proc. 2nd ACM Conf. on Electronic Commerce, pp. 158–167. ACM (2000)

    Google Scholar 

  17. Valiant, L.G.: Parallelism in comparison problems. SIAM J. on Computing 4(3), 348–355 (1975)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel

    Yuval Carmel & Boaz Patt-Shamir

Authors
  1. Yuval Carmel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Boaz Patt-Shamir
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept of Mathematics and Comp Science, Paderborn University, Paderborn, Nordrhein-Westfalen, Germany

    Christian Scheideler

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Carmel, Y., Patt-Shamir, B. (2015). Comparison-Based Interactive Collaborative Filtering. In: Scheideler, C. (eds) Structural Information and Communication Complexity. SIROCCO 2015. Lecture Notes in Computer Science(), vol 9439. Springer, Cham. https://doi.org/10.1007/978-3-319-25258-2_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25258-2_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25257-5

  • Online ISBN: 978-3-319-25258-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation