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SPAA
2009
ACM
16 years 5 months ago
Beyond nested parallelism: tight bounds on work-stealing overheads for parallel futures
Work stealing is a popular method of scheduling fine-grained parallel tasks. The performance of work stealing has been extensively studied, both theoretically and empirically, but...
Daniel Spoonhower, Guy E. Blelloch, Phillip B. Gib...
KDD
2009
ACM
192views Data Mining» more  KDD 2009»
16 years 5 months ago
Learning optimal ranking with tensor factorization for tag recommendation
Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or de...
Steffen Rendle, Leandro Balby Marinho, Alexandros ...
KDD
2009
ACM
174views Data Mining» more  KDD 2009»
16 years 5 months ago
Audience selection for on-line brand advertising: privacy-friendly social network targeting
This paper describes and evaluates privacy-friendly methods for extracting quasi-social networks from browser behavior on user-generated content sites, for the purpose of finding ...
Foster J. Provost, Brian Dalessandro, Rod Hook, Xi...
KDD
2009
ACM
194views Data Mining» more  KDD 2009»
16 years 5 months ago
Combining link and content for community detection: a discriminative approach
In this paper, we consider the problem of combining link and content analysis for community detection from networked data, such as paper citation networks and Word Wide Web. Most ...
Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu
KDD
2009
ACM
180views Data Mining» more  KDD 2009»
16 years 5 months ago
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Sofus A. Macskassy
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