Demo Data from EDBT 2026 / reviewers in the wild / expert
Gianni Morini
dblp:12/3705
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2003
—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% | |
| Theoretical computer science
1 paper |
Mathematical optimization · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval
ranking |
0.0 | 1 | 2003 | Adaptive ranking of web pages · WWW 2003 |
Information retrieval › ranking › search ranking
web ranking |
0.0 | 1 | 2003 | Adaptive ranking of web pages · WWW 2003 |
Mathematical optimization › continuous optimization › nonlinear optimization
quadratic programming |
0.0 | 1 | 2003 | Adaptive ranking of web pages · WWW 2003 |
Methods — techniques the papers use, named apart from their topics
quadratic programming · 0.1clustering · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Adaptive ranking of web pagesabstractIn this paper, we consider the possibility of altering the PageRank of web pages, from an administrator's point of view, through the modification of the PageRank equation. It is shown that this problem can be solved using the traditional quadratic programming techniques. In addition, it is shown that the number of parameters can be reduced by clustering web pages together through simple clustering techniques. This problem can be formulated and solved using quadratic programming techniques. It is demonstrated experimentally on a relatively large web data set, viz., the WT10G, that it is possible to modify the PageRanks of the web pages through the proposed method using a set of linear constraints. It is also shown that the PageRank of other pages may be affected; and that the quality of the result depends on the clustering technique used. It is shown that our results compared well with those obtained by a HITS based method. Ah Chung Tsoi, Gianni Morini, Franco Scarselli, Markus Hagenbuchner, Marco Maggini |
WWW | 2 |