Predicting Customer Models Using Behavior-Based Features in Shops
Identifieur interne : 000547 ( Main/Exploration ); précédent : 000546; suivant : 000548Predicting Customer Models Using Behavior-Based Features in Shops
Auteurs : Junichiro Mori [Japon] ; Yutaka Matsuo [Japon] ; Hitoshi Koshiba [Japon] ; Kenro Aihara [Japon] ; Hideaki Takeda [Japon]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2009.
English descriptors
- Teeft :
- Active rfid readers, Activity recognition, Adaptive information services, Attribute, Behavior data, Behavior history, Behavior pattern, Behavior patterns, Best performance feature, Blog, Blog system, Blog template, Card readers, Certain value, Context markup language userml, Context ontology, Customer attributes, Customer model, Customer models, Customer visits, Duration sequence, Feature selection, Frequency duration sequence, Gender, General user model, Heidelberg, Information services, Learner, Location history, Location information, Matrix, Mobile device, Mobile environments, Model people, Modeling, Negative comments, Numerous studies, Online system, Participant, Potential applications, Recent years, Results show, Sensor, Sensor data, Several features, Several studies, Springer, Subjective sentiment, Temporal granularity, Time duration, Training examples, Transition matrix, Ubiquitous, Ubiquitous user modeling, User, User attributes, User data, User information, User model, User modeling, User models, Value precision, Value precision frequency, Various types, Video cameras, Weighted features, Wireless access points.
Abstract
Abstract: 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.
Url:
DOI: 10.1007/978-3-642-02247-0_14
Affiliations:
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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. 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