Demo Data from SIGIR 2026 / reviewers in the wild / expert
Kenneth Shores
dblp:154/6802
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
—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 |
Web and social media mining · 77% Information retrieval · 23% | |
| Software engineering, system software, and programming languages
1 paper |
Empirical software engineering · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Web and social media mining
web analytics |
0.2 | 1 | 2015 | User Session Identification Based on Strong Regularities in Inter-activity Time · WWW 2015 |
Empirical software engineering
user behavior analysis |
0.2 | 1 | 2015 | User Session Identification Based on Strong Regularities in Inter-activity Time · WWW 2015 |
Methods — techniques the papers use, named apart from their topics
temporal rhythm analysis · 0.4clustering · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | User Session Identification Based on Strong Regularities in Inter-activity TimeabstractSession identification is a common strategy used to develop metrics for web analytics and perform behavioral analyses of user-facing systems. Past work has argued that session identification strategies based on an inactivity threshold is inherently arbitrary or has advocated that thresholds be set at about 30 minutes. In this work, we demonstrate a strong regularity in the temporal rhythms of user initiated events across several different domains of online activity (incl. video gaming, search, page views and volunteer contributions). We describe a methodology for identifying clusters of user activity and argue that the regularity with which these activity clusters appear implies a good rule-of-thumb inactivity threshold of about 1 hour. We conclude with implications that these temporal rhythms may have for system design based on our observations and theories of goal-directed human activity. Aaron Halfaker, Oliver Keyes, Daniel Kluver, Jacob Thebault-Spieker, Tien T. Nguyen, Kenneth Shores, Anuradha Uduwage, Morten Warncke-Wang |
WWW | 6 |