Demo Data from VLDB 2026 / reviewers in the wild / expert
Yunfei (Jesse) Yao
dblp:296/6480
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0001-9831-5083ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Theory of computation · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Privacy and data protection · 100% | |
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Privacy and data protection
privacy regulation |
0.8 | 1 | 2024 | Privacy and Polarization: An Inference-Based Framework · EC 2024 |
Methods — techniques the papers use, named apart from their topics
inference framework · 1.5empirical analysis · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Privacy and Polarization: An Inference-Based FrameworkabstractDigital publishers increasingly use advertising as a monetization strategy. At the core of ad-based monetization is behavioral ad targeting, which creates a sustainable revenue stream for publishers and keeps online content mostly free. However, behavioral ad targeting requires the collection and use of consumer-level data, which leads to privacy concerns among consumers. Although the main intent of privacy regulations is to safeguard consumer privacy, a consistent finding from past empirical research is that privacy regulation hurts digital publishers. Specifically, prior research suggests that the revenue loss due to privacy regulations is more pronounced for general interest (vs. specialized) publishers, who have greater uncertainty about their consumer types without consumer tracking. For example, in the absence of consumer tracking, a mainstream news website such as the New York Times - which attracts a highly heterogeneous pool of readers - has a harder time inferring consumer types and interests in order to show them relevant ads, compared to a niche, ideologically extreme website such as Infowars. Tommaso Bondi, Omid Rafieian, Yunfei (Jesse) Yao |
EC | 3 |