Demo Data from EDBT 2026 / reviewers in the wild / expert
Carlos Shui Zhang
dblp:297/0625
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2022
0000-0001-6464-8912ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Databases, data management, data science and information retrieval · 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.
| Databases, data mining, and information retrieval
1 paper |
Recommender systems · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Recommender systems › domain-specific recommendation
educational recommender system |
0.5 | 1 | 2021 | The Winner Takes it All: Geographic Imbalance and Provider (Un)fairness in Educational Recommender Systems · SIGIR 2021 |
Recommender systems
fairness-aware recommendation |
0.5 | 1 | 2021 | The Winner Takes it All: Geographic Imbalance and Provider (Un)fairness in Educational Recommender Systems · SIGIR 2021 |
Recommender systems › fairness-aware recommendation
provider fairness |
0.5 | 1 | 2021 | The Winner Takes it All: Geographic Imbalance and Provider (Un)fairness in Educational Recommender Systems · SIGIR 2021 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Enabling cross-continent provider fairness in educational recommender systems
Elizabeth Gómez, Carlos Shui Zhang, Ludovico Boratto, Maria Salamó, Guilherme Ramos |
Future Gener. Comput. Syst. | 2 |
| 2021 | The Winner Takes it All: Geographic Imbalance and Provider (Un)fairness in Educational Recommender SystemsabstractEducational recommender systems channel most of the research efforts on the effectiveness of the recommended items. While teachers have a central role in online platforms, the impact of recommender systems for teachers in terms of the exposure such systems give to the courses is an under-explored area. In this paper, we consider data coming from a real-world platform and analyze the distribution of the recommendations w.r.t. the geographical provenience of the teachers. We observe that data is highly imbalanced towards the United States, in terms of offered courses and of interactions. These imbalances are exacerbated by recommender systems, which overexpose the country w.r.t. its representation in the data, thus generating unfairness for teachers outside that country. To introduce equity, we propose an approach that regulates the share of recommendations given to the items produced in a country (visibility) and the position of the items in the recommended list (exposure). Elizabeth Gómez, Carlos Shui Zhang, Ludovico Boratto, Maria Salamó, Mirko Marras |
SIGIR | 2 |