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CHI
2007
ACM
14 years 6 months ago
How it works: a field study of non-technical users interacting with an intelligent system
In order to develop intelligent systems that attain the trust of their users, it is important to understand how users perceive such systems and develop those perceptions over time...
Joe Tullio, Anind K. Dey, Jason Chalecki, James Fo...
EDBT
2009
ACM
277views Database» more  EDBT 2009»
13 years 11 months ago
G-hash: towards fast kernel-based similarity search in large graph databases
Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and simila...
Xiaohong Wang, Aaron M. Smalter, Jun Huan, Gerald ...
FLAIRS
2010
13 years 8 months ago
Handling of Numeric Ranges for Graph-Based Knowledge Discovery
Nowadays, graph-based knowledge discovery algorithms do not consider numeric attributes (they are discarded in the preprocessing step, or they are treated as alphanumeric values w...
Oscar E. Romero, Jesus A. Gonzalez, Lawrence B. Ho...
IJSWIS
2006
168views more  IJSWIS 2006»
13 years 6 months ago
Unraveling the Taste Fabric of Social Networks
Popular online social networks such as Friendster and MySpace do more than simply reveal the superficial structure of social connectedness--the rich meanings bottled within social...
Hugo Liu, Pattie Maes, Glorianna Davenport
KDD
2009
ACM
210views Data Mining» more  KDD 2009»
14 years 6 months ago
Large-scale behavioral targeting
Behavioral targeting (BT) leverages historical user behavior to select the ads most relevant to users to display. The state-of-the-art of BT derives a linear Poisson regression mo...
Ye Chen, Dmitry Pavlov, John F. Canny