Skip to main content
SpringerLink
Log in

Introduction

  • Chapter
  • First Online:
Semantics in Adaptive and Personalised Systems

  • 257 Accesses

Abstract

Adaptive and personalized systems play an increasingly important role in our daily lives, since we more and more rely on systems that tailor their behavior on the ground of our preferences and needs, and support us in a broad range of heterogeneous decision-making tasks.

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 EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
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

Notes

  1. 1.

    https://www.visualcapitalist.com/internet-minute-2018/.

  2. 2.

    Alvin Toffler first proposed the portmanteau “prosumers” in the book “Third Wave” in 1980.

  3. 3.

    https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read.

  4. 4.

    https://merchdope.com/youtube-stats/.

  5. 5.

    http://www.mobithinking.com/mobile-marketing-tools/latest-mobile-stats.

  6. 6.

    http://blog.nielsen.com/nielsenwire/social/.

  7. 7.

    The study was carried out in 2010. It is likely that the amount of information today available would make this ratio even higher.

  8. 8.

    http://googleblog.blogspot.com/2009/12/personalized-search-for-everyone.html.

  9. 9.

    http://archive.fortune.com/magazines/fortune/fortune_archive/2006/11/27/8394347/index.htm.

  10. 10.

    In most of the scenarios that will be discussed in this book documents and items can be considered as synonyms, since we will always describe the items by providing them with some descriptive features. However, it is necessary to state that this is not a constraint, and RS can also work without exploiting content.

  11. 11.

    http://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers.

  12. 12.

    In collaborative filtering methodologies, two users sharing similar preferences are labeled as neighbors.

  13. 13.

    In a real collaborative filtering scenario, a neighbor typically consists of tens or hundreds of users.

  14. 14.

    This problem is typically referred to as cold start.

  15. 15.

    http://ec.europa.eu/justice/data-protection/reform/files/regulation_oj_en.pdf.

  16. 16.

    Textual content needs to be properly processed in order to extract such an information from the rough text. More details on a typical natural language processing pipeline that can be adopted in such a scenario are provided in Chap. 2.

References

  1. Baeza-Yates R, Ribeiro-Neto B (1999) Modern information retrieval, vol 463. ACM press, New York

    Google Scholar 

  2. Batmaz Z, Yurekli A, Bilge A, Kaleli C (2018) A review on deep learning for recommender systems: challenges and remedies. Artif Intell Rev, 1–37

    Article  Google Scholar 

  3. Bawden D, Robinson L (2009) The dark side of information: overload, anxiety and other paradoxes and pathologies. J Inf Sci 35(2):180–191

    Article  Google Scholar 

  4. Belkin NJ, Croft WB (1992) Information filtering and information retrieval: two sides of the same coin? Commun ACM 35(12):29–38

    Article  Google Scholar 

  5. Berry MW, Dumais ST, Letsche TA (1995) Computational methods for intelligent information access. In: Proceedings of the 1995 ACM/IEEE conference on supercomputing, ACM, p 20

    Google Scholar 

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

    Google Scholar 

  7. Cramer H, Evers V, Ramlal S, Van Someren M, Rutledge L, Stash N, Aroyo L, Wielinga B (2008) The effects of transparency on trust in and acceptance of a content-based art recommender. User Model User Adapt Interact 18(5):455–496

    Article  Google Scholar 

  8. Eppler MJ, Mengis J (2004) The concept of information overload: a review of literature from organization science, accounting, marketing, MIS, and related disciplines. Inf Soc 20(5):325–344

    Article  Google Scholar 

  9. Goodman B, Flaxman S (2016) European union regulations on algorithmic decision-making and a right to explanation. arXiv preprint arXiv:160608813

  10. Grau J (2009) Personalized product recommendations: predicting shoppers’ needs. https://twinklemagazine.nl/2009/05/EMarketer_Etailers_worstelen_met_aanbevelingen/eMarketer_personalization_report.pdf

  11. Hallowell EM (2005) Overloaded circuits. Harvard business review, p 11

    Google Scholar 

  12. Hanani U, Shapira B, Shoval P (2001) Information filtering: overview of issues, research and systems. User modeling and user-adapted interaction 11(3):203–259

    Article  Google Scholar 

  13. Ho J, Tang R (2001) Towards an optimal resolution to information overload: an infomediary approach. In: Proceedings of the 2001 international ACM SIGGROUP conference on supporting group work, ACM, pp 91–96

    Google Scholar 

  14. Ingwersen P, Willett P (1995) An introduction to algorithmic and cognitive approaches for information retrieval. Libri 45(3–4):160–177

    Google Scholar 

  15. Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems: an introduction. Cambridge University Press

    Google Scholar 

  16. Karakoulas G, Semeraro G (1999) Machine learning for intelligent information access. In: Advanced course on artificial intelligence. Springer, Berlin, pp 274–280

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  18. Koren Y, Bell R (2015) Advances in collaborative filtering. In: Recommender systems handbook. Springer, Berlin, pp 77–118

    Chapter  Google Scholar 

  19. Lee D, Hosanagar K (2014) Impact of recommender systems on sales volume and diversity. Proceedings of the International Conference on Information Systems—Building a Better World through Information Systems, ICIS 2014, pp 880–894

    Google Scholar 

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

    Article  Google Scholar 

  21. Lops P, De Gemmis M, Semeraro G (2011) Content-based recommender systems: state of the art and trends. In: Recommender systems handbook. Springer, Berlin, pp 73–105

    Google Scholar 

  22. Malone TW, Grant KR, Turbak FA, Brobst SA, Cohen MD (1987) Intelligent information-sharing systems. Commun ACM 30(5):390–402. https://doi.org/10.1145/22899.22903

    Article  Google Scholar 

  23. Mulvenna MD, Anand SS, Büchner AG (2000) Personalization on the net using web mining: introduction. Commun ACM 43(8):122–125

    Article  Google Scholar 

  24. Ott AC (2010) The 24-hour customer: new rules for winning in a time-starved, always-connected economy. HarperBusiness

    Google Scholar 

  25. Pathak B, Garfinkel R, Gopal RD, Venkatesan R, Yin F (2010) Empirical analysis of the impact of recommender systems on sales. J Manag Inf Syst 27(2):159–188

    Article  Google Scholar 

  26. Pilászy I, Tikk D (2009) Recommending new movies: even a few ratings are more valuable than metadata. In: Proceedings of the third ACM conference on recommender systems, ACM, pp 93–100

    Google Scholar 

  27. Rescher N (1960) Choice without preference. A study of the history and of the logic of the problem of Buridan ass. Kant-Studien 51(1–4):142–175

    Article  Google Scholar 

  28. Resnick P, Varian HR (1997) Recommender systems. Commun ACM 40(3):56–58

    Article  Google Scholar 

  29. Ricci F, Rokach L, Shapira B (2015) Recommender systems: introduction and challenges. In: Recommender systems handbook. Springer, Berlin, pp 1–34

    Chapter  Google Scholar 

  30. Salton G, McGill M (1983) Introduction to modern information retrieval. McGraw-Hill, New York

    MATH  Google Scholar 

  31. Schrage M (2016) How the big data explosion has changed decision making. Harvard Business Review

    Google Scholar 

  32. Schwartz B (2004) The paradox of choice: why more is less. Ecco, New York

    Google Scholar 

  33. Sheridan TB, Ferrell WR (1974) Man-machine systems. Information, control, and decision models of human performance. The MIT Press

    Google Scholar 

  34. Shirky C (2008) It’s not information overload. It’s filter failure. Web 2.0 Expo

    Google Scholar 

  35. Sinha R, Swearingen K (2002) The role of transparency in recommender systems. In: CHI’02 extended abstracts on human factors in computing systems, ACM, pp 830–831

    Google Scholar 

  36. Tintarev N, Masthoff J (2007) A survey of explanations in recommender systems. In: 2007 IEEE 23rd international conference on data engineering workshop, IEEE, pp 801–810

    Google Scholar 

  37. Toffler A (1970) Future shock, a disturbing and challenging book. Random House

    Google Scholar 

  38. Waddington P (1997) Dying for information? A report on the effects of information overload in the UK and worldwide. British library research and innovation report, pp 49–52

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università di Bari Aldo Moro, Bari, Italy

    Pasquale Lops

  2. Dipartimento di Informatica, Università di Bari Aldo Moro, Bari, Italy

    Cataldo Musto, Fedelucio Narducci & Giovanni Semeraro

Authors
  1. Pasquale Lops
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cataldo Musto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fedelucio Narducci
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Giovanni Semeraro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pasquale Lops .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Lops, P., Musto, C., Narducci, F., Semeraro, G. (2019). Introduction. In: Semantics in Adaptive and Personalised Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-05618-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05618-6_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05617-9

  • Online ISBN: 978-3-030-05618-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation