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 | |
| 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 | Taiwan, Province of China |
| City | Taipei |
| Period | 17/3/27 → 17/3/29 |
Fingerprint
Keywords
- Family Structure Estimation
- Purchasing Data
ASJC Scopus subject areas
- Engineering(all)
Cite this
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
}
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
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.
ER -