Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturing (PNTIM 2019)

Shopping Recommendation System Design Based on Deep Learning

Authors
Li Haihan, Qi Guanglei, He Nana, Dong Xinri
Corresponding Author
Li Haihan
Available Online November 2019.
DOI
10.2991/pntim-19.2019.81How to use a DOI?
Keywords
Machine Learning; Recommended Algorithm; Pathon; Jingdong Mall
Abstract

With the development of internet shopping, the amount of user data generated is increasing day by day. In this paper, a shopping recommendation system based on deep learning is constructed. The user data crawling module and shopping recommendation module are mainly designed. Firstly, obtain important user review information and product information from Jingdong Mall by python crawler and build a user data crawling module. Then a shopping recommendation system was constructed based on deep learning, combined with recommendation algorithm, The system extracts the characteristics of users and commodities through neural network algorithms, proposing a coupled recommendation algorithm (referred to as U-S recommendation algorithm) based on user characteristics and product similarity. The algorithm calculates the best match rate between users and commodities. The results show that the proposed algorithm can improve the effectiveness of the recommendation system, compared with the algorithm based on similarity of products.

Copyright
© 2019, 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 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturing (PNTIM 2019)
Series
Atlantis Highlights in Engineering
Publication Date
November 2019
ISBN
978-94-6252-829-1
ISSN
2589-4943
DOI
10.2991/pntim-19.2019.81How to use a DOI?
Copyright
© 2019, 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  - Li Haihan
AU  - Qi Guanglei
AU  - He Nana
AU  - Dong Xinri
PY  - 2019/11
DA  - 2019/11
TI  - Shopping Recommendation System Design Based on Deep Learning
BT  - Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturing (PNTIM 2019)
PB  - Atlantis Press
SP  - 397
EP  - 401
SN  - 2589-4943
UR  - https://doi.org/10.2991/pntim-19.2019.81
DO  - 10.2991/pntim-19.2019.81
ID  - Haihan2019/11
ER  -