Abstract
Over the past years, fashion-related challenges have gained a lot of attention in the research community. Outfit generation and recommendation, i.e., the composition of a set of items of different types (e.g., tops, bottom, shoes, accessories) that go well together, are among the most challenging ones. That is because items have to be both compatible amongst each other and also personalized to match the taste of the customer. Recently there has been a plethora of work targeted at tackling these problems by adopting various techniques and algorithms from the machine learning literature. However, to date, there is no extensive comparison of the performance of the different algorithms for outfit generation and recommendation. In this paper, we close this gap by providing a broad evaluation and comparison of various algorithms, including both personalized and non-personalized approaches, using online, real-world user data from one of Europe’s largest fashion stores. We present the adaptations we made to some of those models to make them suitable for personalized outfit generation. Moreover, we provide insights for models that have not yet been evaluated on this task, specifically, GPT, BERT and Seq-to-Seq LSTM.
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Notes
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The exact number of forward passes for sampling a single outfit from BERT depends on the implementation. We found it to be ideally at least an order of magnitude higher than the outfit length.
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A random algorithm would result in close to 100% personalization rate.
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Celikik, M. et al. (2021). Outfit Generation and Recommendation—An Experimental Study. In: Dokoohaki, N., Jaradat, S., Corona Pampín, H.J., Shirvany, R. (eds) Recommender Systems in Fashion and Retail. Lecture Notes in Electrical Engineering, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-030-66103-8_7
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