Leveraging Information from Sponsored Advertising at Online Retail Marketplaces

Long, Fei; Jerath, Kinshuk; Sárváry, Miklós

doi:10.2139/ssrn.3516104

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Katona and Zhu (2017) show how quality scores can incentivize advertisers to invest in their landing pages and to improve their conversion rates. Long, Jerath, and Sarvary (2018) study the informational role of search advertising on the organic rankings of an online retail platform. Our work differs from these articles because we study display advertising auctions in RTB.…”

Section: Related Literaturementioning

confidence: 99%

First-Price Auctions in Online Display Advertising

Despotakis¹,

Ravi²,

Sayedi³

2021

Journal of Marketing Research

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We link the rapid and dramatic move from second-price to first-price auction format in the display advertising market to the move from the waterfalling mechanism employed by publishers for soliciting bids in a pre-ordered cascade over exchanges, to an alternate header bidding strategy that broadcasts the request for bid to all exchanges simultaneously. First, we argue that the move by the publishers from waterfalling to header bidding was a revenue improving move for publishers in the old regime when exchanges employed second-price auctions. Given the publisher move to header bidding, we show that exchanges move from second-price to first-price auctions to increase their expected clearing prices. Interestingly, when all exchanges move to first-price auctions, each exchange faces stronger competition from other exchanges and some exchanges may end up with lower revenue than when all exchanges use second-price auctions; yet, all exchanges move to first-price auctions in the unique equilibrium of the game. We show that the new regime hinders the exchanges’ ability to differentiate in equilibrium. Furthermore, it allows the publishers to achieve the revenue of the optimal mechanism despite not having direct access to the advertisers.

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Section: Related Literaturementioning

confidence: 99%

First-Price Auctions in Online Display Advertising

Despotakis¹,

Ravi²,

Sayedi³

2021

Journal of Marketing Research

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“…Overall, IBK and Bagging associated 45.1% and 43% queries to the correct user with above 70% precision for the noisy dataset, while J48, KStar, and LMT associated 42.2%, 41.7%, and 40.6% queries to the correct user with the precision of 70.9%, 73.5%, and 70.2%. Similarly, in the clean dataset, IBK and Bagging associated 79.5% and 75.7% queries to the correct user with 79.6% and 75.9% precision, while J48, KStar, and LMT associated 73.9%, 74.4%, and 72% queries to the correct user with the precision of 73.9%, 6 Scientific Programming 76.1%, and 72.6%.…”

Section: Resultsmentioning

confidence: 92%

“…However, while using the web search services, the user usually posts their physical condition and health information as a query [3]. Web search engines claim that they collect and maintain user queries as user profile for various activities such as result ranking [4], market research [3], personalization [5], targeted advertisements [6], and others. On the brighter side, maintaining users profile can actually improve the quality of results and user experience, while on the darker side, this indiscriminate collection of users' queries may cause critical privacy breaches as users' queries may contain sensitive and personal information [7].…”

Section: Introductionmentioning

confidence: 99%

QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search

Khan

Ahmad

Alsayed

et al. 2020

Scientific Programming

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With the advancement in ICT, web search engines have become a preferred source to find health-related information published over the Internet. Google alone receives more than one billion health-related queries on a daily basis. However, in order to provide the results most relevant to the user, WSEs maintain the users’ profiles. These profiles may contain private and sensitive information such as the user’s health condition, disease status, and others. Health-related queries contain privacy-sensitive information that may infringe user’s privacy, as the identity of a user is exposed and may be misused by the WSE and third parties. This raises serious concerns since the identity of a user is exposed and may be misused by third parties. One well-known solution to preserve privacy involves issuing the queries via peer-to-peer private information retrieval protocol, such as useless user profile (UUP), thereby hiding the user’s identity from the WSE. This paper investigates the level of protection offered by UUP. For this purpose, we present QuPiD (query profile distance) attack: a machine learning-based attack that evaluates the effectiveness of UUP in privacy protection. QuPiD attack determines the distance between the user’s profile (web search history) and upcoming query using our proposed novel feature vector. The experiments were conducted using ten classification algorithms belonging to the tree-based, rule-based, lazy learner, metaheuristic, and Bayesian families for the sake of comparison. Furthermore, two subsets of an America Online dataset (noisy and clean datasets) were used for experimentation. The results show that the proposed QuPiD attack associates more than 70% queries to the correct user with a precision of over 72% for the clean dataset, while for the noisy dataset, the proposed QuPiD attack associates more than 40% queries to the correct user with 70% precision.

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Are consumers averse to sponsored messages? The role of search advertising in information discovery

Sahni,

Zhang

2023

Quant Mark Econ

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Leveraging Information from Sponsored Advertising at Online Retail Marketplaces

Cited by 5 publications

References 25 publications

First-Price Auctions in Online Display Advertising

First-Price Auctions in Online Display Advertising

QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search

Are consumers averse to sponsored messages? The role of search advertising in information discovery

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