Improvement of E-Commerce Recommendation Systems with Deep Hybrid Collaborative Filtering with Content: A Case Study

Wójcik Filip () and Górnik Michał ()
Additional contact information
Wójcik Filip: Wroclaw University of Economics and Business, Faculty of Management, Wrocław, Poland
Górnik Michał: Wroclaw University of Economics and Business, Faculty of Economics and Finance, Wrocław, Poland

Econometrics. Advances in Applied Data Analysis, 2020, vol. 24, issue 3, 37-50

Abstract: This paper presents a proposition to utilize flexible neural network architecture called Deep Hybrid Collaborative Filtering with Content (DHCF) as a product recommendation engine. Its main goal is to provide better shopping suggestions for customers on the e-commerce platform. The system was tested on 2018 Amazon Reviews Dataset, using repeated cross validation and compared with other approaches: collaborative filtering (CF) and deep collaborative filtering (DCF) in terms of mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). DCF and DHCF were proved to be significantly better than the CF. DHCF proved to be better than DCF in terms of MAE and MAPE, it also scored the best on separate test data. The significance of the differences was checked by means of a Friedman test, followed by post-hoc comparisons to control p-value. The experiment shows that DHCF can outperform other approaches considered in the study, with more robust scores.

Keywords: collaborative filtering; deep learning; content model; product recommendation (search for similar items in EconPapers)
JEL-codes: C45 C53 C55 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.15611/eada.2020.3.03 (text/html)

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:vrs:eaiada:v:24:y:2020:i:3:p:37-50:n:3

DOI: 10.15611/eada.2020.3.03

Access Statistics for this article

Econometrics. Advances in Applied Data Analysis is currently edited by Józef Dziechciarz

More articles in Econometrics. Advances in Applied Data Analysis from Sciendo
Bibliographic data for series maintained by Peter Golla ().