Proceedings of the 8th International Conference on Social Network, Communication and Education (SNCE 2018)

Design of Clothing Clustering Recommendation System on SOM Neural Network

Authors
Hao Ma, Hao Liu
Corresponding Author
Hao Ma
Available Online May 2018.
DOI
10.2991/snce-18.2018.121How to use a DOI?
Keywords
Personalized recommendation; SOM network; Clothing cluster recommendation
Abstract

The clothing recommendation system can record users' personalized clothing information and select records, complete the clothing recommendation function for users, and show great potential in solving personalized recommendation. In this paper, a personalized clothing recommendation method based on SOM neural network is proposed in this paper. This method analyzes the customer's personalized demand, seasonal factors and subjective factors, and carries out the clothing clustering recommendation according to the customers' choice. The test results show that the SOM neural network is used for the classification function of the clothing recommendation system, which has good accuracy and reliability.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 8th International Conference on Social Network, Communication and Education (SNCE 2018)
Series
Advances in Computer Science Research
Publication Date
May 2018
ISBN
978-94-6252-505-4
ISSN
2352-538X
DOI
10.2991/snce-18.2018.121How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Hao Ma
AU  - Hao Liu
PY  - 2018/05
DA  - 2018/05
TI  - Design of Clothing Clustering Recommendation System on SOM Neural Network
BT  - Proceedings of the 8th International Conference on Social Network, Communication and Education (SNCE 2018)
PB  - Atlantis Press
SP  - 591
EP  - 595
SN  - 2352-538X
UR  - https://doi.org/10.2991/snce-18.2018.121
DO  - 10.2991/snce-18.2018.121
ID  - Ma2018/05
ER  -