Abstract
The recent success of distinct e-commerce systems has driven many fashion companies into the online marketplace, allowing consumers to quickly and easily access a worldwide network of brands. However, to succeed in this scenario, it is necessary to provide a tailored, personalized, and reliable fashion shopping experience. Moreover, unfortunately, current solutions on marketing have provided a general approach to push and suggest the most popular or purchased items in most cases. Thus, this paper proposes a new ensemble recommendation system based on the stacking of classical approaches associated with contextual information about customers and products. Our idea is to incorporate user preferences and item characteristics to ensure a desirable level of personalization to commonly applied methods. Our method is a Neural Collaborative Filtering algorithm that can combine any recommendation system with contextual domain information. The results are promising, showing significant gains of up to 80% MRR, 70% NDCG, and 108% Hits when compared to popular baselines for this same scenario.
Supported by CAPES, CNPq, Finep, Fapesp and Fapemig.
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Werneck, H. et al. (2022). A Stacking Recommender System Based on Contextual Information for Fashion Retails. In: Gervasi, O., Murgante, B., Hendrix, E.M.T., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2022. ICCSA 2022. Lecture Notes in Computer Science, vol 13375. Springer, Cham. https://doi.org/10.1007/978-3-031-10522-7_38
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