Abstract
Recommender systems become widely used on the web, especially in e-commerce. In online fashion platforms, personalization becomes prevalent as user’s decisions are driven not only by textual features but also the aesthetic features of products. We propose a novel machine learning approach that makes personalized fashion recommendations based on aesthetic and descriptive features of fashion products. We also introduce a new model that discovers the hidden correlations between fashion products that are frequently bought together as pairs. Our experiments on four real fashion datasets demonstrated that our aesthetic-aware recommender can achieve recommendations with accuracy up-to 89% when compared to related fashion recommenders.
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Zhou, B., Suleiman, B., Yaqub, W. (2021). Aesthetic-Aware Recommender System for Online Fashion Products. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13108. Springer, Cham. https://doi.org/10.1007/978-3-030-92185-9_24
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