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EDBT 2026 Demo / reviewers in the wild / expert

Ross Kravitz

dblp:10/10825 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 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 · 100%
Computer graphics and multimedia
1 paper
Multimedia systems and quality of experience · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Recommender systems
session-based recommendation		 0.312017
Predicting Session Length in Media Streaming · SIGIR 2017
Multimedia systems and quality of experience
multimedia streaming		 0.112017
Predicting Session Length in Media Streaming · SIGIR 2017

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

survival analysis · 0.6
YearPublicationVenuePosition
2017 Predicting Session Length in Media Streaming
abstract
Session length is a very important aspect in determining a user's satisfaction with a media streaming service. Being able to predict how long a session will last can be of great use for various downstream tasks, such as recommendations and ad scheduling. Most of the related literature on user interaction duration has focused on dwell time for websites, usually in the context of approximating post-click satisfaction either in search results, or display ads.
Theodore Vasiloudis, Puya Vahabi, Ross Kravitz, Valery Rashkov
 SIGIR3