A Product Similarity Method Based on Deep Confidence Network

DOI

10.2991/msbda-19.2019.22How to use a DOI?

Keywords

Deep confidence network, Product similarity, Deep learning, Product recommendation network, Shortest path

Abstract

To improve product recommendation network, this paper mainly proposes a product similarity calculation algorithm based on deep confidence network. A high-dimensional product is firstly constructed and then input into the DBN model to obtain low-dimensional product feature data. Founded on the low-dimensional product feature data, the similarity between products can be calculated by the cosine formula. Through the data experiment, it is found that as the output dimension of the low-dimensional product feature matrix decreases, the similarity of the product similarity matrix also decreases, which means that the information extracted from the original input matrix is refined, and the effective information of a product is increasing, which means that the information extracted from the original input matrix is refined, and the effective information of a product is increasing.

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  - Hong Liao
AU  - Zhuchao Yu
AU  - Yaxin Cao
AU  - Mengjin Du
AU  - Chengcheng Sun
PY  - 2019/08
DA  - 2019/08
TI  - A Product Similarity Method Based on Deep Confidence Network
BT  - Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019)
PB  - Atlantis Press
SP  - 143
EP  - 147
SN  - 2352-538X
UR  - https://doi.org/10.2991/msbda-19.2019.22
DO  - 10.2991/msbda-19.2019.22
ID  - Liao2019/08
ER  -