The active badge location system
ACM Transactions on Information Systems (TOIS)
The nature of statistical learning theory
The nature of statistical learning theory
Modelling both the Context and the User
Personal and Ubiquitous Computing
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
What we talk about when we talk about context
Personal and Ubiquitous Computing
Consistent Modelling of Users, Devices and Sensors in a Ubiquitous Computing Environment
User Modeling and User-Adapted Interaction
Ontology-Based User Modeling in an Augmented Audio Reality System for Museums
User Modeling and User-Adapted Interaction
Preface to the Special Issue on User Modeling in Ubiquitous Computing
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
Adaptive, intelligent presentation of information for the museum visitor in PEACH
User Modeling and User-Adapted Interaction
Analyzing Museum Visitors' Behavior Patterns
UM '07 Proceedings of the 11th international conference on User Modeling
A Practical Activity Capture Framework for Personal, Lifetime User Modeling
UM '07 Proceedings of the 11th international conference on User Modeling
Using Collaborative Models to Adaptively Predict Visitor Locations in Museums
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Pervasive Personalisation of Location Information: Personalised Context Ontology
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Inferring long-term user properties based on users' location history
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Towards the prediction of user actions on exercises with hints based on survey results
EC-TEL'11 Proceedings of the 6th European conference on Technology enhanced learning: towards ubiquitous learning
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Recent sensor technologies have enabled the capture of users' behavior data. Given the large amount of data currently available from sensor-equipped environments, it is important to attempt characterization of the sensor data for automatically modeling users in a ubiquitous and mobile computing environment. As described herein, we propose a method that predicts a customer model using features based on customers' behavior in a shop. We capture the customers' behavior using various sensors in the form of the time duration and the sequence between blocks in the shop. Based on behavior data from the sensors, we design features that characterize the behavior pattern of a customer in the shop. We employ those features using a machine learning approach to predict customer attributes such as age, gender, occupation, and interest. Our results show that our designed behavior-based features perform with F -values of 70---90% for prediction. We also discuss the potential applications of our method in user modeling.