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Rohan Seth

dblp:05/2678 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2008
—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
Recommender systems · 75% Web and social media mining · 25%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Recommender systems
graph-based recommendation		 0.112008
Video suggestion and discovery for youtube: taking random walks through the view graph · WWW 2008
Recommender systems › graph-based recommendation
random walk recommendation		 0.112008
Video suggestion and discovery for youtube: taking random walks through the view graph · WWW 2008
Recommender systems
video recommendation		 0.112008
Video suggestion and discovery for youtube: taking random walks through the view graph · WWW 2008

Methods — techniques the papers use, named apart from their topics

random walk · 0.1preference propagation · 0.1
YearPublicationVenuePosition
2008 Video suggestion and discovery for youtube: taking random walks through the view graph
abstract
The rapid growth of the number of videos in YouTube provides enormous potential for users to find content of interest to them. Unfortunately, given the difficulty of searching videos, the size of the video repository also makes the discovery of new content a daunting task. In this paper, we present a novel method based upon the analysis of the entire user-video graph to provide personalized video suggestions for users. The resulting algorithm, termed Adsorption, provides a simple method to efficiently propagate preference information through a variety of graphs. We extensively test the results of the recommendations on a three month snapshot of live data from YouTube.
Shumeet Baluja, Rohan Seth, Yushi Jing, Jay Yagnik, Shankar Kumar, Deepak Ravichandran, Mohamed Aly 0002
 WWW2