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

Schaefer, Maximilian; Sapi, Geza; Lörincz, Szabolcs

doi:10.2139/ssrn.3161325

Cited by 6 publications

(3 citation statements)

References 24 publications

(12 reference statements)

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“…However, diminishing returns are not necessarily the case when we look at the impact of an increase in the size of the data sample on the recommendation quality in a search engine environment. Schaefer et al (2018) illustrate that additional data from previous searches on the same keyword tends to improve the quality of search results. If initial quality is not high, then the increase in the size of the data sample can have a greater impact than diminishing returns might suggest.…”

Section: Market Power In Digital Markets: Potential Solutionsmentioning

confidence: 95%

Digital Platforms and Antitrust

2020

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Georgios Petropoulos gratefully acknowledges financial support from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 799093. This paper was finalised in May 2020. It has been selected as a chapter contribution for the forthcoming Oxford Handbook of Transnational Economic Governance, edited by Eric Brousseau and Jean-Michel Glachant.

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Section: Market Power In Digital Markets: Potential Solutionsmentioning

confidence: 95%

Digital Platforms and Antitrust

2020

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“…The literature on big data in economics is still extremely scarce. The few exceptions are empirical studies such asLambrecht and Tucker (2015),Bajari et al (2019),and Schaefer et al (2018), as well as theory papers such asRubinfeld and Gal (2017),Prufer and Schottmüller (2017) andDeCorniere and Taylor (2019).…”

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confidence: 99%

Merger Policy in Digital Markets: AnEx PostAssessment†

Argentesi

Buccirossi

Calvano

et al. 2020

Journal of Competition Law &Amp; Economics

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This paper presents a broad retrospective evaluation of mergers and merger decisions in markets dominated by multisided digital platforms. First, we document almost 300 acquisitions carried out by three major tech companies—Amazon, Facebook, and Google—between 2008 and 2018. We cluster target companies on their area of economic activity providing suggestive evidence on the strategies behind these mergers. Second, we discuss the features of digital markets that create new challenges for competition policy. By using relevant case studies as illustrative examples, we discuss theories of harm that have been used or, alternatively, could have been formulated by authorities in these cases. Finally, we retrospectively examine two important merger cases, Facebook/Instagram and Google/Waze, providing a systematic assessment of the theories of harm considered by the UK competition authorities as well as evidence on the evolution of the market after the transactions were approved. We discuss whether the competition authority performed complete and careful analyses to foresee the competitive consequences of the investigated mergers and whether a more effective merger control regime can be achieved within the current legal framework.

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“…We also add to a small existing literature on the value of data which has focused on marketing problems. For the quality of recommendations in online search and retail, Schaefer, Sapi, and Lorincz (2018) and Yoganarasimhan (2019) find the amount and detail of data can confer important competitive advantage while Bajari, Chernozhukov, Hortaçsu, and Suzuki (2019) argue that improved forecasting technologies are key. Claussen, Peukert, and Sen (2019) quantify the economic returns to data for the recommendation of online news.…”

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confidence: 99%

The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing

2021

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Large-scale data show promise to provide efficiency gains through individualized risk predictions in many business and policy settings. Yet, assessments of the degree of data-enabled efficiency improvements remain scarce. We quantify the value of the availability of a variety of data combinations for tackling the policy problem of curbing antibiotic resistance, where the reduction of inefficient antibiotic use requires improved diagnostic prediction. Focusing on antibiotic prescribing for suspected urinary tract infections in primary care in Denmark, we link individual-level administrative data with microbiological laboratory test outcomes to train a machine learning algorithm predicting bacterial test results. For various data combinations, we assess out of sample prediction quality and efficiency improvements due to prediction-based prescription policies. The largest gains in prediction quality can be achieved using simple characteristics such as patient age and gender or patients' health care data. However, additional patient background data lead to further incremental policy improvements even though gains in prediction quality are small. Our findings suggest that evaluating prediction quality against the ground truth only may not be sufficient to quantify the potential for policy improvements.

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

Abstract: The working papers published in the Series constitute work in progress circulated to stimulate discussion and critical comments. Views expressed represent exclusively the authors' own opinions and do not necessarily reflect those of the editor.

Cited by 6 publications

References 24 publications

Digital Platforms and Antitrust

Digital Platforms and Antitrust

Merger Policy in Digital Markets: AnEx PostAssessment†

The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing

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