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» How to Evaluate Models of User Affect
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AINA
2008
IEEE
14 years 2 months ago
Opportunistic Data Dissemination Using Real-World User Mobility Traces
— Opportunistic communication allows humans equipped with mobile devices to exchange information via a wireless link whenever they are nearby. This work examines the performance ...
Andreas Heinemann, Jussi Kangasharju, Max Müh...
EPIA
2009
Springer
13 years 11 months ago
Item-Based and User-Based Incremental Collaborative Filtering for Web Recommendations
Abstract. In this paper we propose an incremental item-based collaborative filtering algorithm. It works with binary ratings (sometimes also called implicit ratings), as it is typi...
Catarina Miranda, Alípio Mário Jorge
USAB
2010
13 years 6 months ago
"Same Same but Different" How Service Contexts of Mobile Technologies Shape Usage Motives and Barriers
As wireless technologies evolve, mobile technologies and services will increasingly affect our lives, accompanied by positive and negative effects. This development requires a high...
Katrin Arning, Sylvia Gaul, Martina Ziefle
CRITICAL
2005
13 years 9 months ago
Affect: from information to interaction
While affective computing explicitly challenges the primacy of rationality in cognitivist accounts of human activity, at a deeper level it relies on and reproduces the same inform...
Kirsten Boehner, Rogério de Paula, Paul Dou...
AIHC
2007
Springer
14 years 1 months ago
Affect Detection and an Automated Improvisational AI Actor in E-Drama
Enabling machines to understand emotions and feelings of the human users in their natural language textual input during interaction is a challenging issue in Human Computing. Our w...
Li Zhang, Marco Gillies, John A. Barnden, Robert J...