User-Adapted Recommendation of Content on Mobile Devices Using Bayesian Networks

Hirotoshi IWASAKI; Nobuhiro MIZUNO; Kousuke HARA; Yoichi MOTOMURA

doi:10.1587/transinf.E93.D.1186

User-Adapted Recommendation of Content on Mobile Devices Using Bayesian Networks

Hirotoshi IWASAKI, Nobuhiro MIZUNO, Kousuke HARA, Yoichi MOTOMURA

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Mobile devices, such as cellular phones and car navigation systems, are essential to daily life. People acquire necessary information and preferred content over communication networks anywhere, anytime. However, usability issues arise from the simplicity of user interfaces themselves. Thus, a recommendation of content that is adapted to a user's preference and situation will help the user select content. In this paper, we describe a method to realize such a system using Bayesian networks. This user-adapted mobile system is based on a user model that provides recommendation of content (i.e., restaurants, shops, and music that are suitable to the user and situation) and that learns incrementally based on accumulated usage history data. However, sufficient samples are not always guaranteed, since a user model would require combined dependency among users, situations, and contents. Therefore, we propose the LK method for modeling, which complements incomplete and insufficient samples using knowledge data, and CPT incremental learning for adaptation based on a small number of samples. In order to evaluate the methods proposed, we applied them to restaurant recommendations made on car navigation systems. The evaluation results confirmed that our model based on the LK method can be expected to provide better generalization performance than that of the conventional method. Furthermore, our system would require much less operation than current car navigation systems from the beginning of use. Our evaluation results also indicate that learning a user's individual preference through CPT incremental learning would be beneficial to many users, even with only a few samples. As a result, we have developed the technology of a system that becomes more adapted to a user the more it is used.

Publication: IEICE TRANSACTIONS on Information Vol.E93-D No.5 pp.1186-1196

Publication Date: 2010/05/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E93.D.1186

Type of Manuscript: PAPER

Category: Artificial Intelligence, Data Mining

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Hirotoshi IWASAKI, Nobuhiro MIZUNO, Kousuke HARA, Yoichi MOTOMURA, "User-Adapted Recommendation of Content on Mobile Devices Using Bayesian Networks" in IEICE TRANSACTIONS on Information, vol. E93-D, no. 5, pp. 1186-1196, May 2010, doi: 10.1587/transinf.E93.D.1186.
Abstract: Mobile devices, such as cellular phones and car navigation systems, are essential to daily life. People acquire necessary information and preferred content over communication networks anywhere, anytime. However, usability issues arise from the simplicity of user interfaces themselves. Thus, a recommendation of content that is adapted to a user's preference and situation will help the user select content. In this paper, we describe a method to realize such a system using Bayesian networks. This user-adapted mobile system is based on a user model that provides recommendation of content (i.e., restaurants, shops, and music that are suitable to the user and situation) and that learns incrementally based on accumulated usage history data. However, sufficient samples are not always guaranteed, since a user model would require combined dependency among users, situations, and contents. Therefore, we propose the LK method for modeling, which complements incomplete and insufficient samples using knowledge data, and CPT incremental learning for adaptation based on a small number of samples. In order to evaluate the methods proposed, we applied them to restaurant recommendations made on car navigation systems. The evaluation results confirmed that our model based on the LK method can be expected to provide better generalization performance than that of the conventional method. Furthermore, our system would require much less operation than current car navigation systems from the beginning of use. Our evaluation results also indicate that learning a user's individual preference through CPT incremental learning would be beneficial to many users, even with only a few samples. As a result, we have developed the technology of a system that becomes more adapted to a user the more it is used.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.1186/_p

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@ARTICLE{e93-d_5_1186,
author={Hirotoshi IWASAKI, Nobuhiro MIZUNO, Kousuke HARA, Yoichi MOTOMURA, },
journal={IEICE TRANSACTIONS on Information},
title={User-Adapted Recommendation of Content on Mobile Devices Using Bayesian Networks},
year={2010},
volume={E93-D},
number={5},
pages={1186-1196},
abstract={Mobile devices, such as cellular phones and car navigation systems, are essential to daily life. People acquire necessary information and preferred content over communication networks anywhere, anytime. However, usability issues arise from the simplicity of user interfaces themselves. Thus, a recommendation of content that is adapted to a user's preference and situation will help the user select content. In this paper, we describe a method to realize such a system using Bayesian networks. This user-adapted mobile system is based on a user model that provides recommendation of content (i.e., restaurants, shops, and music that are suitable to the user and situation) and that learns incrementally based on accumulated usage history data. However, sufficient samples are not always guaranteed, since a user model would require combined dependency among users, situations, and contents. Therefore, we propose the LK method for modeling, which complements incomplete and insufficient samples using knowledge data, and CPT incremental learning for adaptation based on a small number of samples. In order to evaluate the methods proposed, we applied them to restaurant recommendations made on car navigation systems. The evaluation results confirmed that our model based on the LK method can be expected to provide better generalization performance than that of the conventional method. Furthermore, our system would require much less operation than current car navigation systems from the beginning of use. Our evaluation results also indicate that learning a user's individual preference through CPT incremental learning would be beneficial to many users, even with only a few samples. As a result, we have developed the technology of a system that becomes more adapted to a user the more it is used.},
keywords={},
doi={10.1587/transinf.E93.D.1186},
ISSN={1745-1361},
month={May},}

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TY - JOUR
TI - User-Adapted Recommendation of Content on Mobile Devices Using Bayesian Networks
T2 - IEICE TRANSACTIONS on Information
SP - 1186
EP - 1196
AU - Hirotoshi IWASAKI
AU - Nobuhiro MIZUNO
AU - Kousuke HARA
AU - Yoichi MOTOMURA
PY - 2010
DO - 10.1587/transinf.E93.D.1186
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2010
AB - Mobile devices, such as cellular phones and car navigation systems, are essential to daily life. People acquire necessary information and preferred content over communication networks anywhere, anytime. However, usability issues arise from the simplicity of user interfaces themselves. Thus, a recommendation of content that is adapted to a user's preference and situation will help the user select content. In this paper, we describe a method to realize such a system using Bayesian networks. This user-adapted mobile system is based on a user model that provides recommendation of content (i.e., restaurants, shops, and music that are suitable to the user and situation) and that learns incrementally based on accumulated usage history data. However, sufficient samples are not always guaranteed, since a user model would require combined dependency among users, situations, and contents. Therefore, we propose the LK method for modeling, which complements incomplete and insufficient samples using knowledge data, and CPT incremental learning for adaptation based on a small number of samples. In order to evaluate the methods proposed, we applied them to restaurant recommendations made on car navigation systems. The evaluation results confirmed that our model based on the LK method can be expected to provide better generalization performance than that of the conventional method. Furthermore, our system would require much less operation than current car navigation systems from the beginning of use. Our evaluation results also indicate that learning a user's individual preference through CPT incremental learning would be beneficial to many users, even with only a few samples. As a result, we have developed the technology of a system that becomes more adapted to a user the more it is used.
ER -