How much can behavioral targeting help online advertising?
By Jun Yan, Ning Liu, Gang Wang, Wen Zhang, Yun Jiang, Zheng Chen. The Web Conference, 2009
397 citations recorded by the source corpus.
Subfields: Information Systems, Marketing, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/3168dcbdce9ba0f8076133da4ffa6e6732484a4a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Krishnaram Kenthapadi
- Nisheeth K. Vishnoi
- L. Elisa Celis
- Aleksandra Korolova
- Giuseppe Ateniese
- Richard Shay
- Nina Mishra
- Ilya Mironov
In Marketing
- Nisheeth K. Vishnoi
- L. Elisa Celis
- Lorrie Faith Cranor
- Ayman Farahat
- Aleksandra Korolova
- Yang Wang
- Pedro Giovanni Leon
In Sociology and Political Science
- Krishnaram Kenthapadi
- Aleksandra Korolova
- Nisheeth K. Vishnoi
- L. Elisa Celis
- Yang Wang
- Ilya Mironov
- Nina Mishra
- Lorrie Faith Cranor
Citations in the corpus
- Third-Party Web Tracking: Policy and Technology (2012, background): “[83], has been widely miscited by supporters of third-party tracking.”
- How effective is targeted advertising? (2012, methodology): “[23] offer one of the first looks into whether there is any value in targeting in online media.”
- Smart, useful, scary, creepy: perceptions of online behavioral advertising (2012, background): “Targeting advertising based on web searches has been shown to increase ad click-through rates considerably when compared with untargeted ads [28].”
- No NAT'd User Left Behind: Fingerprinting Users behind NAT from NetFlow Records Alone (2014, background): “Their basic [34], [35], [32], [4], [16], [24] [36], [12] Traffic and Application Profiling [3], [25], [28] [37], [23], [15], [29] [6] Information Leaks [19], [30], [5], [33] Device Profiling [17], [11], [8] technique though cannot be used to re-identify users later on.”
- Toward Controlling Discrimination in Online Ad Auctions (2019, background): “Overall, such targeted advertising leads to higher utilities for the advertisers who show content to relevant audiences, for the users who view related advertisements, and for the platform which can benefit from selling targeted advertisements [22, 23, 26, 54].”
- Privacy via the Johnson-Lindenstrauss Transform (2012, background): “There is convincing evidence that segmentation of users into clusters is effective in some contexts [4,25,27,33].”
- Real-time bidding for online advertising: measurement and analysis (2013, neutral): “The understanding of user is usually referred to as behaviour targeting, where ad networks utilise the browsing history of a specific user to infer his interests, as well as geographical location, local time, etc for target matching [32, 23, 30].”
- Protecting privacy when mining and sharing user data (2012, background): “In a broader context, there is evidence that narrowly targeted ads are much more e↵ective than ordinary ones [143,195] and that very targeted audience buying ads, e.”