Skip to main content
SpringerLink
Log in

Optimizing User Information Value in a Web Search Through the Whittle Index

  • Conference paper
  • First Online:
CSEI: International Conference on Computer Science, Electronics and Industrial Engineering (CSEI) (CSEI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 678))

  • 241 Accesses

Abstract

Attention economy is a rich information management approach in order to get only significant information. In this work, we analyze the problem of optimizing the value of information presented in an electronic device to users who seek information on the web and whose attention is a priori limited and considered as a scarce and valuable resource. The optimization problem is posed as a dynamic and stochastic prioritization problem and is modeled as a dual-speed multi-armed restless bandit problem (RMABP) in a finite state-space and discrete-time setting. In addition, Adaptive-Greedy algorithm (AG) is used to approximate their solution, this algorithm assigns the value of Whittle’s index to each piece of information, which determines whether or not it is favorable to be presented to the user at a given time. Computational experiments based on Monte Carlo modeling are presented, which show that this methodology substantially improves Greedy index policy and asymptotically approximates the optimization solution to Whittle benchmark.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Notes

  1. 1.

    This policy gives i the set of links shown in the top list for each time period.

  2. 2.

    Total indicates the sum over all time periods.

References

  1. Brown, P.J., et al.: The perversion of certainty: choice architecture, digital paternalism and virtual validation in the attention economy. In: Proceedings of the International Annual Conference of the American Society for Engineering Management, pp. 1–7. American Society for Engineering Management (ASEM) (2018)

    Google Scholar 

  2. Brynjolfsson, E., Oh, J.: The attention economy: measuring the value of free digital services on the internet. In: ICIS (2012). https://aisel.aisnet.org/icis2012/proceedings/EconomicsValue/9

  3. Caiza, G., Bologna, J.K., Garcia, C.A., Garcia, M.V.: Industrial training platform using augmented reality for instrumentation commissioning. In: De Paolis, L.T., Bourdot, P. (eds.) AVR 2020. LNCS, vol. 12243, pp. 268–283. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58468-9_20

    Chapter  Google Scholar 

  4. Cho, J., Roy, S.: Impact of search engines on page popularity. In: Proceedings of the 13th international conference on World Wide Web, pp. 20–29. ACM (2004). https://doi.org/10.1145/988672.988676

  5. Davenport, T., Beck, J.: The Attention Economy: Understanding the New Currency of Business. Harvard Business School Press, Boston (2013)

    Google Scholar 

  6. Falkinger, J.: Attention economies. J. Econ. Theory 133(1), 266–294 (2007). https://doi.org/10.1016/j.jet.2005.12.001

    Article  MATH  Google Scholar 

  7. Glazebrook, K.D., Niño-Mora, J., Ansell, P.S.: Index policies for a class of discounted restless bandits. Adv. Appl. Probab. 34(4), 754–774 (2002). https://doi.org/10.1239/aap/1037990952

    Article  MathSciNet  MATH  Google Scholar 

  8. Hinz, O., Hill, S., Kim, J.Y.: TV’s dirty little secret: the negative effect of popular TV on online auction sales. MIS Q. 40(3), 623–644 (2016). https://www.jstor.org/stable/26629030

  9. Huberman, B.A., Wu, F.: The economics of attention: maximizing user value in information-rich environments. Adv. Complex Syst. 11(04), 487–496 (2008). https://doi.org/10.1142/s0219525908001830

    Article  MathSciNet  MATH  Google Scholar 

  10. Klimov, G.: Time-sharing service systems. i. Theory Probab. Appl. 19(3), 532–551 (1975). https://doi.org/10.1137/1119060

  11. Niño-Mora, J.: Restless bandits, partial conservation laws and indexability. Adv. Appl. Probab. 33(1), 76–98 (2001). https://doi.org/10.1017/s0001867800010648

    Article  MathSciNet  MATH  Google Scholar 

  12. Niño-Mora, J.: Dynamic allocation indices for restless projects and queueing admission control: a polyhedral approach. Math. Program. 93(3), 361–413 (2002). https://doi.org/10.1007/s10107-002-0362-6

    Article  MathSciNet  MATH  Google Scholar 

  13. Niño-Mora, J.: Restless bandit marginal productivity indices, diminishing returns, and optimal control of make-to-order/make-to-stock m/g/1 queues. Math. Oper. Res. 31(1), 50–84 (2006). https://doi.org/10.1287/moor.1050.0165

    Article  MathSciNet  MATH  Google Scholar 

  14. Niño-Mora, J.: Dynamic priority allocation via restless bandit marginal productivity indices. TOP 15(2), 161–198 (2007). https://doi.org/10.1007/s11750-007-0025-0

    Article  MathSciNet  MATH  Google Scholar 

  15. Niño-Mora, J.: Multi-armed restless bandits, index policies, and dynamic priority allocation. Boletín de Estadística e Investigación Operativa 26(2), 124–133 (2010)

    MathSciNet  MATH  Google Scholar 

  16. Pandey, S., Roy, S., Olston, C., Cho, J., Chakrabarti, S.: Shuffling a stacked deck: the case for partially randomized ranking of search engine results. In: Proceedings of the 31st International Conference on Very Large Data Bases, pp. 781–792. VLDB Endowment (2005)

    Google Scholar 

  17. Papadimitriou, C.H., Tsitsiklis, J.N.: The complexity of optimal queueing network control. In: Proceedings of IEEE 9th Annual Conference on Structure in Complexity Theory, pp. 318–322. IEEE (1994). https://doi.org/10.1109/sct.1994.315792

  18. Servia-Rodríguez, S., Huberman, B., Asur, S.: Deciding what to display: maximizing the information value of social media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 9, pp. 13–21 (2015)

    Google Scholar 

  19. Shapiro, C., Varian, H.: Information Rules: A Strategic Guide to the Network Economy. Harvard Business Press, Boston (2013)

    Google Scholar 

  20. Simon, H., Laird, J.: The Sciences of the Artificial, Reissue of the Third Edition with a New Introduction by John Laird. MIT Press, Cambridge (2019)

    Google Scholar 

  21. Weber, R.R., Weiss, G.: On an index policy for restless bandits. J. Appl. Probab. 27(3), 637–648 (1990). https://doi.org/10.2307/3214547

    Article  MathSciNet  MATH  Google Scholar 

  22. Whittle, P.: Restless bandits: activity allocation in a changing world. J. Appl. Probab. 25(A), 287–298 (1988). https://doi.org/10.2307/3214163

Download references

Author information

Authors and Affiliations

  1. Universidad Privada del Norte, Lima, Peru

    German Mendoza-Villacorta

  2. Universidad Nacional Mayor de San Marcos, Lima, Peru

    Yony-Raúl Santaria-Leuyacc

Authors
  1. German Mendoza-Villacorta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yony-Raúl Santaria-Leuyacc
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to German Mendoza-Villacorta .

Editor information

Editors and Affiliations

  1. Universidad del País Vasco, Bilbao, Spain

    Marcelo V. Garcia

  2. FISEI, Universidad Técnica de Ambato, Ambato, Ecuador

    Carlos Gordón-Gallegos

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mendoza-Villacorta, G., Santaria-Leuyacc, YR. (2023). Optimizing User Information Value in a Web Search Through the Whittle Index. In: Garcia, M.V., Gordón-Gallegos, C. (eds) CSEI: International Conference on Computer Science, Electronics and Industrial Engineering (CSEI). CSEI 2022. Lecture Notes in Networks and Systems, vol 678. Springer, Cham. https://doi.org/10.1007/978-3-031-30592-4_12

Download citation

Publish with us

Policies and ethics

Search

Navigation