Shopping Recommendation System Design Based on Deep Learning

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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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  -