VLDB 2026 Research / reviewers in the wild / expert
Dave Holtz
dblp:163/6359
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Theory of computation · 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.
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design
mechanism design |
0.2 | 1 | 2015 | Bias and Reciprocity in Online Reviews: Evidence From Field Experiments on Airbnb · EC 2015 |
Algorithmic game theory and mechanism design › incentive mechanism
reputation systems |
0.2 | 1 | 2015 | Bias and Reciprocity in Online Reviews: Evidence From Field Experiments on Airbnb · EC 2015 |
Computational social science and digital humanities
field experiments |
0.1 | 1 | 2015 | Bias and Reciprocity in Online Reviews: Evidence From Field Experiments on Airbnb · EC 2015 |
Methods — techniques the papers use, named apart from their topics
quantitative analysis · 0.4field experiment · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Bias and Reciprocity in Online Reviews: Evidence From Field Experiments on AirbnbabstractReviews and other evaluations are used by consumers to decide what goods to buy and by firms to choose whom to trade with, hire, or promote. However, because potential reviewers are not compensated for submitting reviews and may have reasons to omit relevant information in their reviews, reviews may be biased. We use the setting of Airbnb to study the determinants of reviewing behavior, the extent to which reviews are biased, and whether changes in the design of reputation systems can reduce that bias. We find that reviews on Airbnb are generally informative and 97% of guests privately report having positive experiences. Using two field experiments intended to reduce bias, we show that non-reviewers tend to have worse experiences than reviewers and that strategic reviewing behavior occurred on the site, although the aggregate effect of the strategic behavior was relatively small. We use a quantitative exercise to show that the mechanisms for bias that we document decrease the rate of reviews with negative text and a non-recommendation by just .86 percentage points. Lastly, we discuss how online marketplaces can design more informative review systems. Andrey Fradkin, Elena Grewal, Dave Holtz |
EC | 3 |