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2020 | vol. 24, nr 3 | 37-50

Article title

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


Title variants

Wykorzystanie Hybrydowych Głębokich Sieci Neuronowych jako systemów rekomendacyjnych. Studium przypadku

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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.
W artykule zbadano innowacyjną architekturę sieci neuronowych zwaną Głębokim Hybrydowym Systemem Filtracji Kolaboratywnej (DHCF), mającą posłużyć jako system rekomendacji konsumenckich. Jego zadaniem jest sugerowanie produktów klientom platform e-commerce. System został przetestowany na zbiorze danych 2018 Amazon Reviews, z wykorzystaniem powtórzonej walidacji krzyżowej, i porównany z dwoma innymi podejściami: filtracją kolaboratywną (CF) oraz filtracją kolaboratywną z siecią neuronową (DCF). Do porównania wykorzystano metryki błędu średniokwadratowego (MSE), średniego błędu bezwzględnego (MAE) oraz średniego procentowego błędu bezwzględnego (MAPE). DCF i DHCF uzyskały wyniki istotnie lepsze niż CF, a dodatkowo DHCF uzyskał lepsze wyniki niż DCF pod względem MAE i MAPE. Istotność różnic sprawdzano testem Friedmana z porównaniami wielokrotnymi i kontrolą poziomu istotności. Eksperyment dowodzi, że DHCF uzyskuje lepsze i stabilniejsze wyniki niż pozostałe metody.


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