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The Effect of Big Data on Recommendation Quality: The Example of Internet Search

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  • Maximilian Schäfer
  • Geza Sapi
  • Szabolcs Lorincz

Abstract

Are there economies of scale to data in internet search? This paper is first to use real search engine query logs to empirically investigate how data drives the quality of internet search results. We find evidence that the quality of search results improve with more data on previous searches. Moreover, our results indicate that the type of data matters as well: personalized information is particularly valuable as it massively increases the speed of learning. We also provide some evidence that factors not directly related to data such as the general quality of the applied algorithms play an important role. The suggested methods to disentangle the effect of data from other factors driving the quality of search results can be applied to assess the returns to data in various recommendation systems in e-commerce, including product and information search. We also discuss the managerial, privacy, and competition policy implications of our findings.

Suggested Citation

  • Maximilian Schäfer & Geza Sapi & Szabolcs Lorincz, 2018. "The Effect of Big Data on Recommendation Quality: The Example of Internet Search," Discussion Papers of DIW Berlin 1730, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp1730
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    References listed on IDEAS

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    6. Patrick Bajari & Victor Chernozhukov & Ali Hortaçsu & Junichi Suzuki, 2019. "The Impact of Big Data on Firm Performance: An Empirical Investigation," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 33-37, May.
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Krämer, Jan & Shekhar, Shiva & Hofmann, Janina, 2022. "Regulating Algorithmic Learning in Digital Platform Ecosystems through Data Sharing and Data Siloing: Consequences for Innovation and Welfare," 31st European Regional ITS Conference, Gothenburg 2022: Reining in Digital Platforms? Challenging monopolies, promoting competition and developing regulatory regimes 265645, International Telecommunications Society (ITS).
    2. Graef, Inge & Prüfer, Jens, 2021. "Governance of data sharing: A law & economics proposal," Research Policy, Elsevier, vol. 50(9).
    3. Argentesi, Elena & Buccirossi, Paolo & Calvano, Emilio & Duso, Tomaso & Marrazzo, Alessia & Nava, Salvatore, 2021. "Merger Policy in Digital Markets: An Ex Post Assessment," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 17(1), pages 95-140.
    4. Hemant Bhargava & Antoine Dubus & David Ronayne & Shiva Shekhar, 2024. "The Strategic Value of Data Sharing in Interdependent Markets," CESifo Working Paper Series 10963, CESifo.
    5. de Cornière, Alexandre & Taylor, Greg, 2022. "Data and Competition: a Simple Framework with Applications to Mergers and Market Structure," CEPR Discussion Papers 14446, C.E.P.R. Discussion Papers.
    6. Kesler, Reinhold & Kummer, Michael E. & Schulte, Patrick, 2019. "Competition and privacy in online markets: Evidence from the mobile app industry," ZEW Discussion Papers 19-064, ZEW - Leibniz Centre for European Economic Research.
    7. Arnold, René & Marcus, J. Scott & Petropoulos, Georgios & Schneider, Anna, 2018. "Is data the new oil? Diminishing returns to scale," 29th European Regional ITS Conference, Trento 2018 184927, International Telecommunications Society (ITS).
    8. Calvano, Emilio & Polo, Michele, 2021. "Market power, competition and innovation in digital markets: A survey," Information Economics and Policy, Elsevier, vol. 54(C).
    9. de Cornière, Alexandre & Taylor, Greg, 2020. "Data and Competition: a General Framework with Applications to Mergers, Market Structure, and Privacy Policy," TSE Working Papers 20-1076, Toulouse School of Economics (TSE).
    10. Lenz, Fulko, 2020. "Plattformökonomie – zwischen Abwehr und Wunschdenken," Zeitthemen 03, Stiftung Marktwirtschaft / The Market Economy Foundation, Berlin.
    11. Jörg Claussen & Christian Peukert & Ananya Sen, 2019. "The Editor vs. the Algorithm: Returns to Data and Externalities in Online News," CESifo Working Paper Series 8012, CESifo.
    12. Schaefer, Maximilian & Sapi, Geza, 2023. "Complementarities in learning from data: Insights from general search," Information Economics and Policy, Elsevier, vol. 65(C).
    13. Georgios Petropoulos & Bertin Martens & Geoffrey Parker & Marshall Van Alstyne, 2023. "Platform Competition and Information Sharing," CESifo Working Paper Series 10663, CESifo.
    14. Ehsan Valavi & Joel Hestness & Newsha Ardalani & Marco Iansiti, 2022. "Time and the Value of Data," Papers 2203.09118, arXiv.org.
    15. repec:diw:diwwpp:dp1939 is not listed on IDEAS

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    More about this item

    Keywords

    Big Data; Recommendation quality; Internet search; E-Commerce; Economies of Scale; Search engines;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

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