Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

An Enhanced Semi-supervised Recommendation Model Based on Green’s Function

  • Conference paper
Neural Information Processing. Theory and Algorithms (ICONIP 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6443))

Included in the following conference series:

Abstract

Recommendation, in the filed of machine learning, is known as a technique of identifying user preferences to new items with ratings from recommender systems. Recently, one novel recommendation model using Green’s function treats recommendation as the process of label propagation. Although this model outperforms many standard recommendation methods, it suffers from information loss during graph construction because of data sparsity. In this paper, aiming at solving this problem and improving prediction accuracy, we propose an enhanced semi-supervised Green’s function recommendation model. The main contributions are two-fold: 1) To reduce information loss, we propose a novel graph construction method with global and local consistent similarity; 2) We enhance the recommendation algorithm with the multi-class semi-supervised learning framework. Finally, experimental results on real world data demonstrate the effectiveness of our model.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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.

Similar content being viewed by others

References

  1. Adomavicius, G., Tuzhilin, A.: 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. Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS) 22(1), 177 (2004)

    Article  Google Scholar 

  3. Ding, C., Simon, H., Jin, R., Li, T.: A learning framework using Green’s function and kernel regularization with application to recommender system. In: Proceedings of the 13th ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, p. 269. ACM, New York (2007)

    Google Scholar 

  4. Dueck, D., Frey, B.: Probabilistic sparse matrix factorization. University of Toronto technical report PSI-2004-23 (2004)

    Google Scholar 

  5. Jin, R., Chai, J., Si, L.: An automatic weighting scheme for collaborative filtering. In: Proceedings of the 27th annual international ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 337–344. ACM, New York (2004)

    Google Scholar 

  6. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  7. Ma, H., King, I., Lyu, M.: Effective missing data prediction for collaborative filtering. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, p. 46. ACM, New York (2007)

    Google Scholar 

  8. McLaughlin, M., Herlocker, J.: A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In: Proceedings of the 27th annual international ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 329–336. ACM, New York (2004)

    Google Scholar 

  9. Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. Advances in neural information processing systems 20, 1257–1264 (2008)

    Google Scholar 

  10. Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, p. 295. ACM, New York (2001)

    Google Scholar 

  11. Wang, F., Ma, S., Yang, L., Li, T.: Recommendation on item graphs. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065, pp. 1119–1123. Springer, Heidelberg (2006)

    Google Scholar 

  12. Wang, J., De Vries, A., Reinders, M.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, p. 508. ACM, New York (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wang, D., King, I. (2010). An Enhanced Semi-supervised Recommendation Model Based on Green’s Function. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_49

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17537-4_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17536-7

  • Online ISBN: 978-3-642-17537-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics