Family structure attribute estimation method for product recommendation system

Chiaki Doi; Masaji Katagiri; Takashi Araki; Daizo Ikeda; Hiroshi Shigeno

doi:10.1109/AINA.2017.12

Family structure attribute estimation method for product recommendation system

Chiaki Doi, Masaji Katagiri, Takashi Araki, Daizo Ikeda, Hiroshi Shigeno

情報工学科

研究成果: Conference contribution

抄録

This paper proposes a method that estimatesconsumer family-structure attributesby focusing on purchasing behavior. The method generatesa relevance model for each product type between family-structure attributes and purchasing histories beforehand based on a consumer panel survey. Random Forest, a machine learning method, is employed to generate the model. The proposed method facilitates provisioning of smart recommendations to the consumer family such as suggesting products that reflectthe family structure attributes of the consumer.

本文言語	English
ホスト出版物のタイトル	Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017
出版社	Institute of Electrical and Electronics Engineers Inc.
ページ	167-173
ページ数	7
ISBN（電子版）	9781509060283
DOI	https://doi.org/10.1109/AINA.2017.12
出版ステータス	Published - 2017 5 5
イベント	31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017 - Taipei, Taiwan, Province of China 継続期間: 2017 3 27 → 2017 3 29

Other

Other	31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017
Country	Taiwan, Province of China
City	Taipei
Period	17/3/27 → 17/3/29

ASJC Scopus subject areas

Engineering(all)

文献へのアクセス

10.1109/AINA.2017.12

フィンガープリント「Family structure attribute estimation method for product recommendation system」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル

Doi, C., Katagiri, M., Araki, T., Ikeda, D., & Shigeno, H. (2017). Family structure attribute estimation method for product recommendation system. In Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017 (pp. 167-173). [7920905] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AINA.2017.12

Family structure attribute estimation method for product recommendation system. / Doi, Chiaki; Katagiri, Masaji; Araki, Takashi; Ikeda, Daizo; Shigeno, Hiroshi.

Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 167-173 7920905.

研究成果: Conference contribution

Doi, C, Katagiri, M, Araki, T, Ikeda, D & Shigeno, H 2017, Family structure attribute estimation method for product recommendation system. in Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017., 7920905, Institute of Electrical and Electronics Engineers Inc., pp. 167-173, 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017, Taipei, Taiwan, Province of China, 17/3/27. https://doi.org/10.1109/AINA.2017.12

@inproceedings{2bb063fe1b2245b597f98bac24ad05a6,

title = "Family structure attribute estimation method for product recommendation system",

abstract = "This paper proposes a method that estimatesconsumer family-structure attributesby focusing on purchasing behavior. The method generatesa relevance model for each product type between family-structure attributes and purchasing histories beforehand based on a consumer panel survey. Random Forest, a machine learning method, is employed to generate the model. The proposed method facilitates provisioning of smart recommendations to the consumer family such as suggesting products that reflectthe family structure attributes of the consumer.",

keywords = "Family Structure Estimation, Purchasing Data",

author = "Chiaki Doi and Masaji Katagiri and Takashi Araki and Daizo Ikeda and Hiroshi Shigeno",

year = "2017",

month = may,

day = "5",

doi = "10.1109/AINA.2017.12",

language = "English",

pages = "167--173",

booktitle = "Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017 ; Conference date: 27-03-2017 Through 29-03-2017",

}

TY - GEN

T1 - Family structure attribute estimation method for product recommendation system

AU - Doi, Chiaki

AU - Katagiri, Masaji

AU - Araki, Takashi

AU - Ikeda, Daizo

AU - Shigeno, Hiroshi

PY - 2017/5/5

Y1 - 2017/5/5

N2 - This paper proposes a method that estimatesconsumer family-structure attributesby focusing on purchasing behavior. The method generatesa relevance model for each product type between family-structure attributes and purchasing histories beforehand based on a consumer panel survey. Random Forest, a machine learning method, is employed to generate the model. The proposed method facilitates provisioning of smart recommendations to the consumer family such as suggesting products that reflectthe family structure attributes of the consumer.

AB - This paper proposes a method that estimatesconsumer family-structure attributesby focusing on purchasing behavior. The method generatesa relevance model for each product type between family-structure attributes and purchasing histories beforehand based on a consumer panel survey. Random Forest, a machine learning method, is employed to generate the model. The proposed method facilitates provisioning of smart recommendations to the consumer family such as suggesting products that reflectthe family structure attributes of the consumer.

KW - Family Structure Estimation

KW - Purchasing Data

UR - http://www.scopus.com/inward/record.url?scp=85019742529&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85019742529&partnerID=8YFLogxK

U2 - 10.1109/AINA.2017.12

DO - 10.1109/AINA.2017.12

M3 - Conference contribution

AN - SCOPUS:85019742529

SP - 167

EP - 173

BT - Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017

Y2 - 27 March 2017 through 29 March 2017

ER -