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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 …