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

Department of Information and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	167-173
Number of pages	7
ISBN (Electronic)	9781509060283
DOIs	https://doi.org/10.1109/AINA.2017.12
Publication status	Published - 2017 May 5
Event	31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017 - Taipei, Taiwan, Province of China Duration: 2017 Mar 27 → 2017 Mar 29

Other

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

Keywords

Family Structure Estimation
Purchasing Data

ASJC Scopus subject areas

Engineering(all)

Access to Document

10.1109/AINA.2017.12

Cite this

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.

Research output: Chapter in Book/Report/Conference proceeding › 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 -

Family structure attribute estimation method for product recommendation system

Abstract

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this