Abstract
By carefully recommending selected items to users, recommender systems ought to increase profit from product sales. To achieve this, recommendations need to be relevant, novel and diverse. Many approaches to this problem exist, each with its own advantages and shortcomings. This paper proposes a novel way to combine model, memory and content-based approaches in a cascade-hybrid system, where each approach refines the previous one, sequentially. It is also proposed a straight-forward way to easily incorporate time-awareness into rating matrices. This approach focuses on being intuitive, flexible, robust, auditable and avoid heavy performance costs, as opposed to black-box fashion approaches. Evaluation metrics such as Novelty Score are also formalized and computed, in conjunction with Catalog Coverage and mean recommendation price to better capture the recommender’s performance.
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Acknowledgements
This article is a result of the project “Criação de um Núcleo de I&D para a geração de novo conhecimento nas áreas de Inteligência Artificial, Machine Learning, Intelligent Marketing e One-2-One Marketing”, supported by Operational Programme for Competitiveness and Internationalisation (COMPETE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), for E-goi.
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Rebelo, M.Â., Coelho, D., Pereira, I., Fernandes, F. (2022). A New Cascade-Hybrid Recommender System Approach for the Retail Market. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_36
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