[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Collaborative Filtering and Content-Based Systems

  • Chapter
  • First Online:
Recommender Systems: Algorithms and their Applications

Abstract

In this chapter, the two most widely used types of recommender systems, namely the collaborative filtering method and the content-based system, along with a few of their important sub-types are discussed in this chapter. There are two types of collaborative methods, namely the neighborhood-based and model-based methods. The chapter discusses what are the features of and differences between the two methods. The basic components of the content-based systems are also discussed. Both the systems have their advantages and disadvantages which are also discussed here.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /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
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 119.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
GBP 149.99
Price includes VAT (United Kingdom)
  • 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

  • Aslanian E, Radmanesh M, Jalili M (2016) Hybrid recommender systems based on content feature relationship. IEEE Transactions on Industrial Informatics

    Google Scholar 

  • Beel J, Genzmehr M, Langer S, Nürnberger A, Gipp B (2013) A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation.In: Proceedings of the international workshop on reproducibility and replication in recommender systems evaluation. ACM, pp 7–14

    Google Scholar 

  • Bellogin A, Castells P, Cantador I (2011) Precision-oriented evaluation of recommender systems: an algorithmic comparison. In: Proceedings of RECSYS. ACM, pp 333–336

    Google Scholar 

  • Bobadilla J, Ortega F, Hernando A, Alcalá J (2011) Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowl Based Syst 24(8):1310–1316

    Article  Google Scholar 

  • Bogers T, Van den Bosch A (2008) Recommending scientific articles using citeulike. In: Proceedings of RECSYS. ACM, pp 287–290

    Google Scholar 

  • Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapt Interact 12(4):331–370

    Article  Google Scholar 

  • Chowdhury G (2010) Introduction to modern information retrieval. Facet Publishing, Abingdon

    Google Scholar 

  • Das AS, Datar M, Garg A, Rajaram S (2007) Google news personalization: scalable online collaborative filtering. In: Proceedings of 7 WWW. ACM, pp 271–280

    Google Scholar 

  • Di Noia T, Mirizzi R, Ostuni VC, Romito D, Zanker M (2012.) Linked open data to support content-based recommender systems. In: Proceedings of Semantics. ACM, pp 1–8

    Google Scholar 

  • Fernandes BB, Sacenti JA, Willrich R (2017) Using implicit feedback for neighbors selection: alleviating the sparsity problem in collaborative recommendation systems. In: Proceedings of WEBMEDIA. ACM, pp 341–348

    Google Scholar 

  • Ge M, Delgado-Battenfeld C, Jannach D (2010) Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of RECSYS. ACM, pp 257–260

    Google Scholar 

  • Glauber R, Loula A, Rocha-Junior JB (2013) A mixed hybrid recommender system for given names. ECML PKDD Discov Challenge 2013:25–36

    Google Scholar 

  • Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70

    Article  Google Scholar 

  • Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53

    Article  Google Scholar 

  • Konstan J, Riedl J (2012) Recommender systems: from algorithms to user experience. User Model User-Adapt Interact 22(1):101–123

    Article  Google Scholar 

  • Lathia N, Hailes S, Capra L, Amatriain X (2010) Temporal diversity in recommender systems. In: Proceedings of ACM SIGIR. ACM, pp 210–217

    Google Scholar 

  • Liu Y, Wang S, Khan MS, He J (2018) A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering. Big Data Mining and Analytics 1(3):211–221

    Article  Google Scholar 

  • McNee SM, Riedl J, Konstan JA (2006) Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: Proceedings of CHI. ACM, pp 1097–1101

    Google Scholar 

  • Said A, Bellogín A (2014) Comparative recommender system evaluation: benchmarking recommendation frameworks. In: Proceedings of RECSYS. ACM, pp 129–136

    Google Scholar 

  • Santana LL, Souza AB, Santana DL, Dourado WA, Durão FA (2017) Evaluating ensemble strategies for recommender systems under metadata reduction. In: Proceedings of WEBMEDIA. ACM, pp 125–132

    Google Scholar 

  • Shani G, Gunawardana A (2011) Evaluating recommendation systems. In: Recommender systems handbook. Springer, Berlin, pp 257–297

    Google Scholar 

  • Shardanand U, Maes P (1995) Social information filtering: algorithms for automating “word of mouth”. In: Proceedings of SIGCHI. ACM Press/Addison-Wesley Publishing Co., Boston, MA, pp 210–217

    Google Scholar 

  • Silva DV, Silva RD, Durão FA (2017a) RecStore: recommending stores for shopping mall customers. In: Proceedings of WEBMEDIA. ACM, pp 117–124

    Google Scholar 

  • Silva N, Carvalho D, Pereira AC, Mourão F, Rocha L (2017b) Evaluating different strategies to mitigate the ramp-up problem in recommendation domains. In: Proceedings of WEBMEDIA. ACM, pp 333–340

    Google Scholar 

  • Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009:4

    Article  Google Scholar 

  • Wu D, Zhang G, Lu J (2015) A fuzzy preference treebased recommender system for personalized business-to-business e-services. IEEE Trans Fuzzy Syst 23(1):29–43

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pushpendu Kar .

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kar, P., Roy, M., Datta, S. (2024). Collaborative Filtering and Content-Based Systems. In: Recommender Systems: Algorithms and their Applications. Transactions on Computer Systems and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-97-0538-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-0538-2_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0537-5

  • Online ISBN: 978-981-97-0538-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics