Abstract
In this paper, we present a visual search and recommendation system that supports typical shopping behaviour. We present a unified convolutional neural network architecture, to learn embeddings, which is a way to capture notion of similarity. We will introduce the concept of embeddings with respect to similarity and show how we try to achieve required embeddings with various loss functions. We demonstrate various model architectures based on availability of data. We also demonstrate a semiautomatic way of creating labelled dataset for training. We will talk about the concept of accuracy with respect to similarity, which is complicated as similarity is subjective. Finally, we present an end-to-end system for deployment.
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Kushwaha, A., Chakravorty, S., Das, P. (2021). Style Scanner—Personalized Visual Search and Recommendations. In: Laha, A.K. (eds) Applied Advanced Analytics. Springer Proceedings in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-33-6656-5_5
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DOI: https://doi.org/10.1007/978-981-33-6656-5_5
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