Demo Data from VLDB 2026 / reviewers in the wild / expert
Chang Min Hahn
dblp:217/9639
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1
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.
| Human-computer interaction and pervasive computing
1 paper |
Usability and user experience research · 50% Human-AI interaction · 50% | |
| Network and information security
1 paper |
Privacy and data protection · 100% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Privacy and data protection
online targeting |
0.1 | 1 | 2018 | Unpacking Perceptions of Data-Driven Inferences Underlying Online Targeting and Personalization · CHI 2018 |
Methods — techniques the papers use, named apart from their topics
survey experiment · 0.7online study · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Unpacking Perceptions of Data-Driven Inferences Underlying Online Targeting and PersonalizationabstractMuch of what a user sees browsing the internet, from ads to search results, is targeted or personalized by algorithms that have made inferences about that user. Prior work has documented that users find such targeting simultaneously useful and creepy. We begin unpacking these conflicted feelings through two online studies. In the first study, 306 participants saw one of ten explanations for why they received an ad, reflecting prevalent methods of targeting based on demographics, interests, and other factors. The type of interest-based targeting described in the explanation affected participants' comfort with the targeting and perceptions of its usefulness. We conducted a follow-up study in which 237 participants saw ten interests companies might infer. Both the sensitivity of the interest category and participants' actual interest in that topic significantly impacted their attitudes toward inferencing. Our results inform the design of transparency tools. Claire Dolin, Ben Weinshel, Shawn Shan, Chang Min Hahn, Euirim Choi, Michelle L. Mazurek, Blase Ur |
CHI | 4 |