Predicting Customer Models Using Behavior-based Features in Shops
J. Mori, Y. Matsuo, H. Koshiba, K. Aihara, and H. Takeda. User Modeling, Adaptation, and Personalization: 17th International Conference, UMAP 2009, formerly UM and AH, Trento, Italy, volume 5535 of Lecture Notes in Computer Science, Springer, Berlin, (2009)
DOI: 10.1007/978-3-642-02247-0_14
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 percent for prediction. We also discuss the potential applications of our method in user modeling.
%0 Book Section
%1 MoriMatsuoEtAl09UMAP
%A Mori, Junichiro
%A Matsuo, Yutaka
%A Koshiba, Hitoshi
%A Aihara, Kenro
%A Takeda, Hideaki
%B User Modeling, Adaptation, and Personalization: 17th International Conference, UMAP 2009, formerly UM and AH, Trento, Italy
%C Berlin
%D 2009
%E Houben, Geert-Jan
%E McCalla, Gord
%E Pianesi, Fabio
%E Zancanaro, Massimo
%I Springer
%K 01801 springer paper embedded ai interaction user interface adaptive sensor action data analysis processing product zzz.ami
%P 126--137
%R 10.1007/978-3-642-02247-0_14
%T Predicting Customer Models Using Behavior-based Features in Shops
%V 5535
%X 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 percent for prediction. We also discuss the potential applications of our method in user modeling.
@incollection{MoriMatsuoEtAl09UMAP,
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 percent for prediction. We also discuss the potential applications of our method in user modeling.},
added-at = {2017-06-11T17:21:24.000+0200},
address = {Berlin},
author = {Mori, Junichiro and Matsuo, Yutaka and Koshiba, Hitoshi and Aihara, Kenro and Takeda, Hideaki},
biburl = {https://www.bibsonomy.org/bibtex/23b178e6a508679129463da5328541ebe/flint63},
booktitle = {User Modeling, Adaptation, and Personalization: 17th International Conference, UMAP 2009, formerly UM and AH, Trento, Italy},
crossref = {UMAP2009},
doi = {10.1007/978-3-642-02247-0_14},
editor = {Houben, Geert-Jan and McCalla, Gord and Pianesi, Fabio and Zancanaro, Massimo},
file = {SpringerLink:2009/MoriMatsuoEtAl09UMAP.pdf:PDF},
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keywords = {01801 springer paper embedded ai interaction user interface adaptive sensor action data analysis processing product zzz.ami},
pages = {126--137},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
timestamp = {2018-04-16T12:04:03.000+0200},
title = {Predicting Customer Models Using Behavior-based Features in Shops},
username = {flint63},
volume = 5535,
year = 2009
}