The DOI matches its Crossref record.
First workshop on targeting and ranking for online advertising
Abstract
Online advertising is a rapidly growing, multi-billion dollar industry. It has become a significant element of the Web browsing experience. Online advertising providers use sophisticated ad targeting and ranking algorithms with the dual aim of maximizing revenue while providing a superior user experience. As a result, advertising optimization is a very complex research problem, since it combines relevance with user interaction models, advertiser valuations, and commercial constraints. Online advertising integrates a number of core research areas: machine learning, data mining, search, auction theory, and user modeling.
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Machine learning approaches of recommendation and ranking are active topics of research in the AI community, powered by an enormous quantity of user data continuously gathered through digital platforms.
“Although it is difficult to obtain quantitative data on the impact of AI on e-commerce, the details of which are largely considered sensitive trade secrets, the corresponding machine learning approaches of recommendation and ranking are active topics of research in the AI community, powered by an enormous quantity of user data being continuously gathered through interactions with digital platforms”
From From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate (2025) · cites this paper as [31]
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Luccioni, A. S., Strubell, E., & Crawford, K. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732007 · Cited by 71
“…considered sensitive trade secrets, the corresponding machine learning approaches of recommendation and ranking are active topics of research in the AI community, powered by an enormous quantity of user data being continuously gathered through interactions with digital platforms [19, 20, 31].”
Metadata
- Authors
- Dominowska, E., & Josifovski, V.
- Affiliations
- Microsoft (United States) (US)
- Yahoo (United States) (US)
- ORCID
- Dominowska, Ewa
- Venue
- Proceedings of the 17th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2008-04-21
- Pages
- 1269-1270
- OpenAlex
- W2071373606
- Cited by
- 10
- References
- 0
- Citations by year
- 2025: 1 · 2023: 1 · 2019: 1 · 2015: 1 · 2014: 1
- FWCI
- 1.31
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- online advertising, targeted advertising, ad ranking, advertising optimization, auction theory, user modeling, relevance models, user interaction model, revenue maximization, search engines
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