Demo Data from VLDB 2026 / reviewers in the wild / expert
Hein Roehrig
dblp:42/8024
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1Applied, interdisciplinary, general and emerging 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.
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › query log analysis
clickthrough data |
0.1 | 1 | 2010 | Beyond position bias: examining result attractiveness as a source of presentation bias in clickthrough data · WWW 2010 |
Information retrieval › user behavior › search behavior › click model
position bias |
0.1 | 1 | 2010 | Beyond position bias: examining result attractiveness as a source of presentation bias in clickthrough data · WWW 2010 |
Information retrieval › evaluation › online evaluation
click-based evaluation |
0.0 | 1 | 2010 | Beyond position bias: examining result attractiveness as a source of presentation bias in clickthrough data · WWW 2010 |
Information retrieval
evaluation |
0.0 | 1 | 2010 | Beyond position bias: examining result attractiveness as a source of presentation bias in clickthrough data · WWW 2010 |
Methods — techniques the papers use, named apart from their topics
click-through data analysis · 0.1bias detection · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Beyond position bias: examining result attractiveness as a source of presentation bias in clickthrough dataabstractLeveraging clickthrough data has become a popular approach for evaluating and optimizing information retrieval systems. Although data is plentiful, one must take care when interpreting clicks, since user behavior can be affected by various sources of presentation bias. While the issue of position bias in clickthrough data has been the topic of much study, other presentation bias effects have received comparatively little attention. For instance, since users must decide whether to click on a result based on its summary (e.g., the title, URL and abstract), one might expect clicks to favor "more attractive" results. In this paper, we examine result summary attractiveness as a potential source of presentation bias. This study distinguishes itself from prior work by aiming to detect systematic biases in click behavior due to attractive summaries inflating perceived relevance. Our experiments conducted on the Google web search engine show substantial evidence of presentation bias in clicks towards results with more attractive titles. Yisong Yue, Rajan Patel, Hein Roehrig |
WWW | 3 |