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A Smart Contract Based Recommender System

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Book cover Economics of Grids, Clouds, Systems, and Services (GECON 2019)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 11819))

Abstract

Nowadays information available on the World Wide Web has reached unprecedented growth and it makes difficult for users to find the most relevant for them. In order to alleviate such issue, Recommender Systems (RSs) have been proposed to collect opinions and preferences about a set of items, process such preferences and build a personalized information access.

While the most part of current RSs exploit centralized architecture to provide the service, in this manuscript we propose an alternative approach for building a general purpose RSs that provides to users with more transparent and decentralized rating strategy. Indeed, the proposed framework is built on top of a Distributed Ledger technology platform that runs without any centralized authority and it supports both decentralized ratings and ranking of different items. A preliminary evaluation on the Ethereum test network demonstrates the feasibility of the framework in terms of performance and cost.

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Notes

  1. 1.

    https://www.tripadvisor.com.

  2. 2.

    Statista 2019: https://bit.ly/2BYJf1U.

  3. 3.

    TripAdvisorInsights: https://bit.ly/2UaE8Tn.

  4. 4.

    The protocol starts with a capital “B”, the cryptocurrency does not.

  5. 5.

    Solidity docs: https://bit.ly/2S5X2La.

  6. 6.

    A smart contract function which does not change the contract’s state.

  7. 7.

    Gastroadvisor whitepaper: https://bit.ly/2YfIs6v.

  8. 8.

    Friendz whitepaper: https://bit.ly/2FdKolK.

  9. 9.

    Metamask: https://bit.ly/2DIukHT.

  10. 10.

    Ropsten: https://bit.ly/2E9bfjV.

  11. 11.

    Geth: https://bit.ly/2Isky2b.

  12. 12.

    Infura: https://bit.ly/2L5t4pJ.

  13. 13.

    We remark that a smart contract function which changes the state has to be stored in a transaction, and thus has to be mined.

  14. 14.

    Ropsten blocks, blocksout.com: https://bit.ly/2TQt6C4.

  15. 15.

    Ethereum blocks, blocksout.com: https://bit.ly/2uANLj9.

  16. 16.

    Insight from Etherscan: https://bit.ly/2H6uLB3.

  17. 17.

    Pagination in solidity: https://bit.ly/2CksK0B.

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Author information

Authors and Affiliations

  1. University of Pisa, Largo Bruno Pontecorvo, 3, 56127, Pisa, Italy

    Andrea Lisi & Laura Ricci

  2. University of Palermo, Via Archirafi, 34, 90123, Palermo, Italy

    Andrea De Salve

  3. Consiglio Nazionale delle Ricerche - IIT, via G. Moruzzi, 1, 56124, Pisa, Italy

    Andrea De Salve & Paolo Mori

Authors
  1. Andrea Lisi
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  2. Andrea De Salve
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  3. Paolo Mori
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  4. Laura Ricci
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Corresponding author

Correspondence to Andrea De Salve .

Editor information

Editors and Affiliations

  1. University of Leeds, Leeds, UK

    Karim Djemame

  2. Seoul National University, Seoul, Korea (Republic of)

    Jörn Altmann

  3. University of Zaragoza, Zaragoza, Spain

    José Ángel Bañares

  4. Technion – Israel Institute of Technology and University of Haifa, Haifa, Israel

    Orna Agmon Ben-Yehuda

  5. LUMSA University, Rome, Italy

    Maurizio Naldi

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Lisi, A., De Salve, A., Mori, P., Ricci, L. (2019). A Smart Contract Based Recommender System. In: Djemame, K., Altmann, J., Bañares, J., Agmon Ben-Yehuda, O., Naldi, M. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2019. Lecture Notes in Computer Science(), vol 11819. Springer, Cham. https://doi.org/10.1007/978-3-030-36027-6_3

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  • DOI: https://doi.org/10.1007/978-3-030-36027-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36026-9

  • Online ISBN: 978-3-030-36027-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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