Product advertising recommendation in e-commerce based on deep learning and distributed expression

Lichun Zhou ()
Additional contact information
Lichun Zhou: Shangqiu Normal University

Electronic Commerce Research, 2020, vol. 20, issue 2, No 6, 342 pages

Abstract: Abstract With the advent of Internet big data era, recommendation system has become a hot research topic of information selection. This paper studies the application of deep learning and distributed expression technology in e-commerce product advertising recommendation. In this paper, firstly, from the semantic level of advertising, we build a similarity network based on the theme distribution of advertising, and then build a deep learning model framework for advertising click through rate prediction. Finally, we propose an improved recommendation algorithm based on recurrent neural network and distributed expression. Aiming at the particularity of the recommendation algorithm, this paper improves the traditional recurrent neural network, and introduces a time window to control the hidden layer data transfer of the recurrent neural network. The experimental results show that the improved recurrent neural network model based on time window is superior to the traditional recurrent neural network model in the accuracy of recommendation system. The complexity of calculation is reduced and the accuracy of recommendation system is improved.

Keywords: Deep learning; E-commerce; Distributed expression; Advertising recommendation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://link.springer.com/10.1007/s10660-020-09411-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:elcore:v:20:y:2020:i:2:d:10.1007_s10660-020-09411-6

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10660

DOI: 10.1007/s10660-020-09411-6

Access Statistics for this article

Electronic Commerce Research is currently edited by James Westland

More articles in Electronic Commerce Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().