Skip to main content
SpringerLink
Log in

Case Study IV: Recommender System Using Scalding and Spark

  • Chapter
  • First Online:
Guide to High Performance Distributed Computing

Part of the book series: Computer Communications and Networks ((CCN))

  • 3634 Accesses

Abstract

Recommender Systems are software tools that are used to suggest items of use to users based on certain assumptions [1, 2]. The item here refers to an entity that the system recommends to the users, and accordingly the recommender system’s design, GUI, recommendation technique are dependent on the specific type of item in the discussion.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
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
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover 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. Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM 40(3), 56-58 (1997)

    Google Scholar 

  2. Burke, R.: Hybrid web recommender systems. In: The Adaptive Web, pp. 377-408. Springer Berlin / Heidelberg (2007)

    Google Scholar 

  3. Jannach, D.: Finding preferred query relaxations in content-based recommenders. In: 3rd International IEEE Conference on Intelligent Systems, pp. 355-360 (2006)

    Google Scholar 

  4. Mahmood, T., Ricci, F.: Improving recommender systems with adaptive conversational strategies. In: C. Cattuto, G. Ruffo, F. Menczer (eds.) Hypertext, pp. 73-82. ACM (2009)

    Google Scholar 

  5. McSherry, F., Mironov, I.: Differentially private recommender systems: building privacy into the net. In: KDD 09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 627-636. ACM, New York, NY, USA (2009)

    Google Scholar 

  6. Schwartz, B.: The Paradox of Choice. ECCO, New York (2004)

    Google Scholar 

  7. Ricci, F.: Travel recommender systems. IEEE Intelligent Systems 17(6), 55-57 (2002)

    Google Scholar 

  8. Herlocker, J., Konstan, J., Riedl, J.: Explaining collaborative filtering recommendations. In: In proceedings of ACM 2000 Conference on Computer Supported Cooperative Work, pp. 241-250 (2000)

    Google Scholar 

  9. Brusilovsky, Peter. Methods and techniques of adaptive hypermedia. User modeling and useradapted interaction 6.2-3 (1996): 87-129.

    Google Scholar 

  10. Montaner, M., Lopez, B., de la Rosa, J.L.: A taxonomy of recommender agents on the Internet. Artificial Intelligence Review 19(4), 285-330 (2003)

    Google Scholar 

  11. Fisher, G.: User modeling in human-computer interaction. User Modeling and User-Adapted Interaction 11, 65-86 (2001)

    Google Scholar 

  12. Berkovsky, S., Kuflik, T., Ricci, F.: Mediation of user models for enhanced personalization in recommender systems. User Modeling and User-Adapted Interaction 18(3), 245-286 (2008)

    Google Scholar 

  13. Taghipour, N., Kardan, A., Ghidary, S.S.: Usage-based web recommendations: a reinforcement learning approach. In: Proceedings of the 2007 ACM Conference on Recommender Systems, RecSys 2007, Minneapolis, MN, USA, October 19-20, 2007, pp. 113-120 (2007)

    Google Scholar 

  14. Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: The Adaptive Web, pp. 291-324. Springer Berlin / Heidelberg (2007)

    Google Scholar 

  15. Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. 23 (1), 103-145 (2005)

    Google Scholar 

  16. Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)

    Google Scholar 

  17. Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: applying collaborative filtering to usenet news. Communications of the ACM 40 (3), 77-87 (1997)

    Google Scholar 

  18. Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7 (1), 76-80 (2003)

    Google Scholar 

  19. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. of the 14th Annual Conf. on Uncertainty in Artificial Intelligence, pp. 43-52. Morgan Kaufmann (1998)

    Google Scholar 

  20. Hofmann, T.: Collaborative filtering via Gaussian probabilistic latent semantic analysis. In: SIGIR 03: Proc. of the 26th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 259-266. ACM, New York, NY, USA (2003)

    Google Scholar 

  21. Zitnick, C.L., Kanade, T.: Maximum entropy for collaborative filtering. In: AUAI 04: Proc. of the 20th Conf. on Uncertainty in Artificial Intelligence, pp. 636-643. AUAI Press, Arlington, Virginia, United States (2004)

    Google Scholar 

  22. Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review 13 (5-6), 393-408 (1999)

    Google Scholar 

  23. Bridge, D., Goker, M., McGinty, L., Smyth, B.: Case-based recommender systems. The Knowledge Engineering review 20 (3), 315-320 (2006)

    Google Scholar 

  24. Sinha, R.R., Swearingen, K.: Comparing recommendations made by online systems and friends. In: DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries (2001)

    Google Scholar 

  25. Groh, G., Ehmig, C.: Recommendations in taste related domains: collaborative filtering vs. social filtering. In: GROUP 07: Proceedings of the 2007 international ACM conference on Supporting group work, pp. 127-136. ACM, New York, NY, USA (2007)

    Google Scholar 

  26. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Incremental singular value decomposition algorithms for highly scalable recommender systems. In: Proceedings of the 5th International Conference in Computers and Information Technology (2002)

    Google Scholar 

  27. Ramakrishnan, N., Keller, B.J., Mirza, B.J., Grama, A., Karypis, G.: When being weak is brave: Privacy in recommender systems. IEEE Internet Computing cs.CG/0105028 (2001)

    Google Scholar 

  28. Herlocker, J., Konstan, J., Borchers, A., Riedl, J.. An Algorithmic Framework for Performing Collaborative Filtering. Proceedings of the 1999 Conference on Research and Development in Information Retrieval. Aug. 1999.

    Google Scholar 

  29. Cascading Pipes http://docs.cascading.org/cascading/1.2/javadoc/cascading/pipe/Pipe.html

  30. Singhal, Amit. Modern Information Retrieval: A Brief Overview. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering 24 (4): 35-43, 2001

    Google Scholar 

  31. Tan, Pang-Ning; Steinbach, Michael; Kumar, Vipin, Introduction to Data Mining, ISBN 0-321-32136-7, 2001

    Google Scholar 

  32. Yule, G.U and Kendall, M.G., An Introduction to the Theory of Statistics, 14th Edition (5th Impression 1968). Charles Griffin & Co. pp 258-270, 1950

    Google Scholar 

  33. Cai-Nicolas Ziegler, Book-Crossing dataset, [Online] Available: http://www2.informatik.unifreiburg.de/~cziegler/BX/

Download references

Author information

Authors and Affiliations

  1. M.S. Ramaiah Institute of Technology, Bangalore, Karnataka, India

    K. G. Srinivasa & Anil Kumar Muppalla

Authors
  1. K. G. Srinivasa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anil Kumar Muppalla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. G. Srinivasa .

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Srinivasa, K.G., Muppalla, A.K. (2015). Case Study IV: Recommender System Using Scalding and Spark. In: Guide to High Performance Distributed Computing. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-13497-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-13497-0_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13496-3

  • Online ISBN: 978-3-319-13497-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation