Skip to main content
SpringerLink
Log in

Big Data Analytics and Recommender Systems

  • Chapter
  • First Online:
Big Data Analytics: Systems, Algorithms, Applications

  • 2836 Accesses

Abstract

Recommender systems are designed to augment human decision making. The objective of a recommender system is to suggest relevant items for a user to choose from a plethora of options. In essence, recommender systems are concerned about predicting personalized item choices for a user. Recommender systems produce a ranked list of items ordered in their order of likeability for the user.

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 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

References

  1. Y. Freund, R. Iyer, R.E. Schapire, Y. Singer, An efficient boosting algorithm for combining preferences. The J. Mach. Learn. Res. 4, 933–969 (2003)

    MathSciNet  MATH  Google Scholar 

  2. C. Schmitt, D. Dengler, M. Bauer, Multivariate preference models and decision making with the MAUT machine, in Proceedings of the 9th International Conference on User Modeling (UM 2003), pp. 297–302

    Google Scholar 

  3. J.L. Herlocker, J.A. Konstan, L.G. Terveen, J.T. Riedl, Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)

    Article  Google Scholar 

  4. S. Schiaffino, A. Amandi, Intelligent user profiling, in Artificial Intelligence An International Perspective, ed by M. Bramer. Lecture Notes in Computer Science, vol 5640 (Springer, Berlin, Heidelberg, 2009)

    Google Scholar 

  5. R. Baeza-Yates, C. Hurtado, M. Mendoza, Query recommendation using query logs in search engines, in ed by W. Lindner, M. Mesiti, C. Türker, Y. Tzitzikas, A.I. Vakali, Current Trends in Database Technology—EDBT 2004 Workshops. EDBT 2004. Lecture Notes in Computer Science, vol 3268 (Springer, Berlin, Heidelberg, 2004)

    Google Scholar 

  6. P. Bellekens, G.-J. Houben, L. Aroyo, K. Schaap, A. Kaptein, User model elicitation and enrichment for context-sensitive personalization in a multiplatform TV environment, in Proceedings of the 7th European Conference on Interactive TV and Video (EuroITV’09). (ACM, New York, NY, USA), pp. 119–128 (2009). https://doi.org/10.1145/1542084.1542106

  7. K. Lang, Newsweeder: learning to filter netnews, in Proceedings of the 12th International Conference on Machine Learning (Morgan Kaufmann publishers Inc.: San Mateo, CA, USA, 1995), pp. 331–339

    Google Scholar 

  8. S. Aciar, D. Zhang, S. Simoff, J. Debenham, Informed recommender: basing recommendations on consumer product reviews. IEEE Intell. Syst. 22(3), 39–47 (2007)

    Article  Google Scholar 

  9. S.-H. Yang, B. Long, A.J. Smola, H. Zha, Z. Zheng, Collaborative competitive filtering: learning recommender using context of user choice (2011)

    Google Scholar 

  10. U. Shardanand, P. Maes, Social information filtering: algorithms for automating ‘word of mouth’, in Proceedings Conference Human Factors in Computing Systems, 1995

    Google Scholar 

  11. P Melville, R.J. Mooney, R. Nagarajan, Contentboosted collaborative filtering for improved recommendations. In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI02) (Edmonton, Alberta, 2002), pp. 187–192

    Google Scholar 

  12. R. Burke, B. Mobasher, R. Bhaumik, C. Williams. Segment-based injection attacks against collaborative filtering recommender systems, in ICDM’05: Proceedings of the Fifth IEEE International Conference on Data Mining (Washington, DC, USA, IEEE Computer Society, 2005), pp. 577–580

    Google Scholar 

  13. A.M. Rashid, I. Albert, D. Cosley, S.K. Lam, S.M. McNee, J.A. Konstan, J. Riedl, Getting to know you: learning new user preferences in recommender systems, in Proceedings International Conference Intelligent User Interfaces, 2002

    Google Scholar 

  14. M.J. Pazzani, A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999)

    Article  Google Scholar 

  15. M. Balabanovic, Y. Shoham, Fab: Content-based, collaborative recommendation. Commun. Assoc. Comput. Mach. 40(3), 66–72 (1997)

    Google Scholar 

  16. J.J. Rocchio, Relevance feedback in information retrieval, in SMART Retrieval System—Experiments in Automatic Document Processing, ed by G. Salton, Chapter 14 (Prentice Hall, 1971)

    Google Scholar 

  17. G. Salton, Automatic Text Processing (Addison-Wesley, 1989)

    Google Scholar 

  18. R.J. Mooney, L. Roy, Content-based book recommending using learning for text categorization, in Proceedings ACM SIGIR’99 Workshop Recommender Systems: Algorithms and Evaluation, 1999

    Google Scholar 

  19. J.S. Breese, D. Heckerman, C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering, in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI (July 1998)

    Google Scholar 

  20. X. Su, T.M. Khoshgoftaar, A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 1–20 (2009)

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  23. N. Srebro, T. Jaakkola, Weighted low-rank approximations, in International Conference on Machine Learning (ICDM), 2003

    Google Scholar 

  24. J. Rennie, N. Srebro, Fast maximum margin matrix factorization for collaborative prediction, in International Conference on Machine Learning, 2005

    Google Scholar 

  25. D.D. Lee, H.S. Seung, Learning the parts of objects by non-negative matrix factorization. Nature 401(788) (1999)

    Google Scholar 

  26. P. Cotter, B. Smyth. PTV: intelligent personalized TV guides, in Twelfth Conference on Innovative Applications of Artificial Intelligence, 2000, pp. 957–964

    Google Scholar 

  27. M. Claypool, A. Gokhale, T. Miranda, Combining content-based and collaborative filters in an online newspaper, in Proceedings of the SIGIR-99 Workshop on Recommender Systems: Algorithms and Evaluation, 1999

    Google Scholar 

  28. X. Su, R. Greiner, T.M. Khoshgoftaar, X. Zhu, Hybrid collaborative filtering algorithms using a mixture of experts, in Web Intelligence, 2007, pp. 645–649

    Google Scholar 

  29. G. Shani, A. Gunawardana, Evaluating recommendation systems, in Recommender Systems Handbook, ed. by F. Ricci, L. Rokach, B. Shapira, P. Kantor (Springer, Boston, MA, 2011)

    Google Scholar 

  30. S. Vargas, P. Castells, Rank and relevance in novelty and diversity metrics for recommender systems, in Proceedings of the fifth ACM conference on Recommender systems (RecSys’11). ACM, New York, NY, USA, 109–116 (2011). http://dx.doi.org/10.1145/2043932.2043955

  31. G. Adomavicius, A. Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005). https://doi.org/10.1109/TKDE.2005.99

    Article  Google Scholar 

  32. S.K. Lam, J. Riedl, Shilling recommender systems for fun and profit, in WWW’04: Proceedings of the 13th international conference on World Wide Web (ACM, New York, NY, USA, 2004), pages 393–402

    Google Scholar 

  33. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’11). (ACM, New York, NY, USA), pp. 295–304. http://dx.doi.org/10.1145/2009916.2009959

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

    Article  Google Scholar 

  35. J. Li, O.R. Zaïane, Combining usage, content, and structure data to improve web site recommendation, in Proceedings Fifth International Conference Electronic Commerce and Web Technologies (EC-Web’04), pp. 305–315 (2004)

    Google Scholar 

  36. O.R. Zaïane, J. Srivastava, M. Spiliopoulou, B. M. Masand (eds.), J. Proceedings WEBKDD 2002—Mining Web Data for Discovering Usage Patterns and Profiles, 2003

    Google Scholar 

  37. S.E. Middleton, N.R. Shadbolt, D.C. de Roure, Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. 22(1), 54–88 (2004)

    Article  Google Scholar 

  38. W. Hill, L. Stead, M. Rosenstein, G. Furnas, Recommending and evaluating choices in a virtual community of use, in Proceedings Conference Human Factors in Computing Systems, 1995

    Google Scholar 

  39. P. Resnick, N. Iakovou, M. Sushak, P. Bergstrom, J. Riedl, GroupLens: an open architecture for collaborative filtering of Netnews, in Proceedings 1994 Computer Supported Cooperative Work Conference, 1994

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Informatics Centre, New Delhi, Delhi, India

    Dr. C.S.R. Prabhu

  2. Advanced Analytics Institute, University of Technology, Sydney, Ultimo, NSW, Australia

    Dr. Aneesh Sreevallabh Chivukula

  3. Saarland University, Saarbrücken, Saarland, Germany

    Dr. Aditya Mogadala

  4. Qure.ai, Goregaon East, Mumbai, Maharashtra, India

    Rohit Ghosh

  5. School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

    Dr. L.M. Jenila Livingston

Authors
  1. Dr. C.S.R. Prabhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dr. Aneesh Sreevallabh Chivukula
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dr. Aditya Mogadala
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rohit Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dr. L.M. Jenila Livingston
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C.S.R. Prabhu .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Prabhu, C., Chivukula, A., Mogadala, A., Ghosh, R., Livingston, L. (2019). Big Data Analytics and Recommender Systems. In: Big Data Analytics: Systems, Algorithms, Applications. Springer, Singapore. https://doi.org/10.1007/978-981-15-0094-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-0094-7_14

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0093-0

  • Online ISBN: 978-981-15-0094-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation