Object

Title: Improvement of e-commerce recommendation systems with deep hybrid collaborative filtering with content: A case study

Title in english:

Wykorzystanie Hybrydowych Głębokich Sieci Neuronowych jako systemów rekomendacyjnych. Studium przypadku

Creator:

Wójcik, Filip ; Górnik, Michał

Description:

Econometrics = Ekonometria, 2020, Vol. 24, No. 3, s. 37-50

Abstrakt:

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

Publisher:

Uniwersytet Ekonomiczny we Wrocławiu

Place of publication:

Wrocław

Date:

2020

Resource Type:

artykuł

Resource Identifier:

doi:10.15611/eada.2020.3.03 ; oai:dbc.wroc.pl:83116

Language:

eng

Relation:

Econometrics = Ekonometria, 2020, Vol. 24, No. 3

Access Rights:

Dla wszystkich zgodnie z licencją

License:

CC BY-SA 4.0

Location:

Uniwersytet Ekonomiczny we Wrocławiu

Group publication title:

Ekonometria = Econometrics

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