Galyna Chornous

Department of Economic Cybernetics, Taras Shevchenko National University of Kyiv, Ukraine

chornous@univ.kiev.ua

ORCID 0000-0003-4889-1247

A hybrid user-item-based collaborative filtering model for e-commerce recommendations

Ihor Nikolskyi

Department of Business Automation, BIZZABO LTD

Ukraine 

ihor.nikolskyi@gmail.com

Mateusz Wyszyński

Faculty of Economics and Management, Lazarski University, Warsaw, Poland

mateusz.wyszynski@lazarski.pl 

ORCID 0000-0003-2969-634X

Ganna Kharlamova

Department of Economic Cybernetics, Taras Shevchenko National University of Kyiv

Ukraine

akharlamova@ukr.net

ORCID 0000-0003-3614-712X

Piotr Stolarczyk

Faculty of Economics and Management, Lazarski University, Warsaw, Poland

piotrstolarczyk@o2.pl 

ORCID 0000-0003-0089-7252 

Abstract. The COVID-19 pandemic deepened understanding of e-commerce as an extremely promising sphere. Nowadays, even small businesses are widely using e-shops and e-markets. Thus, small and medium-sized e-commerce companies need powerful, flexible recommender systems, which do not require significant computing and financial resources. Currently, the main vector of such systems developing is targeted at hybridization, i.e. a combination of well-known effective methods. The paper proposes a hybrid model (bagging) to achieve high-quality e-commerce recommendations, which are based on the effective combination of collaborative filtering techniques. The model consists of the following components: User-based collaborative filtering (UBCF, classical variant and method involving text comments to compute the rating matrix), and Item-based collaborative filtering (IBCF) using the attributes of the predicted objects to calculate their similarity. The proposed model can become a methodological basis to introduce the recommender system for the medium-scale e-commerce platforms that are not able to afford deep learning. The recommender system based on this model does not require real-time updates and is easy for website integration. Besides, Root Mean Squared Error (RMSE) of the proposed method is significantly lower than in IBCF and UBCF models. Due to the significant improvement of recommendations accuracy, e-commerce companies will have a chance to positively affect customer loyalty without considerable investment.

Received: February, 2021

1st Revision: October, 2021

Accepted: December, 2021

DOI: 10.14254/2071-8330.2021/14-4/11

JEL Classification: C49, L81, M31

Keywords: recommender system, hybrid model, hybridization, collaborative filtering, rating matrix, item-based technique, user-based technique, e-commerce