Demo Data from SIGIR 2026 / reviewers in the wild / expert
Sudar Muthu
dblp:60/8024
· 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 · 89% Web and social media mining · 11% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › search interfaces
faceted search |
0.1 | 1 | 2010 | Faceted exploration of image search results · WWW 2010 |
Information retrieval › ranking › search ranking
facet ranking |
0.1 | 1 | 2010 | Faceted exploration of image search results · WWW 2010 |
Information retrieval
image retrieval |
0.1 | 1 | 2010 | Faceted exploration of image search results · WWW 2010 |
Information retrieval
retrieval models |
0.1 | 1 | 2010 | Faceted exploration of image search results · WWW 2010 |
Information retrieval
search engines |
0.1 | 1 | 2010 | Faceted exploration of image search results · WWW 2010 |
Web and social media mining
social tagging |
0.0 | 1 | 2010 | Faceted exploration of image search results · WWW 2010 |
Web and social media mining
user-generated content |
0.0 | 1 | 2010 | Faceted exploration of image search results · WWW 2010 |
Methods — techniques the papers use, named apart from their topics
statistical analysis · 0.1query log analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Faceted exploration of image search resultsabstractThis paper describes MediaFaces, a system that enables faceted exploration of media collections. The system processes semi-structured information sources to extract objects and facets, e.g. the relationships between two objects. Next, we rank the facets based on a statistical analysis of image search query logs, and the tagging behaviour of users annotating photos in Flickr. For a given object of interest, we can then retrieve the top-k most relevant facets and present them to the user. The system is currently deployed in production by Yahoo!'s image search engine1. We present the system architecture, its main components, and the application of the system as part of the image search experience. Roelof van Zwol, Börkur Sigurbjörnsson, Ramu Adapala, Lluís Garcia Pueyo, Abhinav Katiyar, Kaushal Kurapati, Mridul Muralidharan, Sudar Muthu, Vanessa Murdock 0001, Polly Ng, Anand Ramani, Anuj Sahai, Sriram Thiru Sathish, Hari Vasudev, Upendra Vuyyuru |
WWW | 8 |