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ORCIDUser Modeling and User-Adapted Interaction, Volume 25
Volume 25, Number 1, March 2015
- Eduardo Vicente-López, Luis M. de Campos
, Juan M. Fernández-Luna, Juan F. Huete, Antonio Tagua-Jiménez, Carmen Tur-Vigil:
An automatic methodology to evaluate personalized information retrieval systems. 1-37 - Rasoul Karimi, Alexandros Nanopoulos, Lars Schmidt-Thieme:
A supervised active learning framework for recommender systems based on decision trees. 39-64 - Nadia Hocine, Abdelkader Gouaïch, Stefano A. Cerri, Denis Mottet, Jérôme Frôger, Isabelle Laffont:
Adaptation in serious games for upper-limb rehabilitation: an approach to improve training outcomes. 65-98
Volume 25, Number 2, June 2015
- Li Chen, Guanliang Chen, Feng Wang:
Recommender systems based on user reviews: the state of the art. 99-154 - Paolo Burelli, Georgios N. Yannakakis:
Adapting virtual camera behaviour through player modelling. 155-183
Volume 25, Number 3, August 2015
- Tsvi Kuflik, Judy Kay, Aaron J. Quigley:
Preface to the special issue on ubiquitous user modeling and user-adapted interaction. 185-187 - Eyal Dim, Tsvi Kuflik, Iris Reinhartz-Berger:
When user modeling intersects software engineering: the info-bead user modeling approach. 189-229 - Masud Moshtaghi, Ingrid Zukerman, R. Andrew Russell:
Statistical models for unobtrusively detecting abnormal periods of inactivity in older adults. 231-265 - Stephan Hammer, Michael Wißner, Elisabeth André:
Trust-based decision-making for smart and adaptive environments. 267-293 - Guanliang Chen, Li Chen:
Augmenting service recommender systems by incorporating contextual opinions from user reviews. 295-329
Volume 25, Number 4, October 2015
- George Papadakis, Ricardo Kawase, Eelco Herder, Wolfgang Nejdl:
Methods for web revisitation prediction: survey and experimentation. 331-369 - Sergey A. Sosnovsky, Peter Brusilovsky:
Evaluation of topic-based adaptation and student modeling in QuizGuide. 371-424
Volume 25, Number 5, December 2015
- Dietmar Jannach, Lukas Lerche, Iman Kamehkhosh, Michael Jugovac:
What recommenders recommend: an analysis of recommendation biases and possible countermeasures. 427-491
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