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arXiv · 2111.00758

Single-Item Fashion Recommender: Towards Cross-Domain Recommendations

Seyed Omid Mohammadi·Hossein Bodaghi·Ahmad Kalhor
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Abstract

Nowadays, recommender systems and search engines play an integral role in fashion e-commerce. Still, many challenges lie ahead, and this study tries to tackle some. This article first suggests a content-based fashion recommender system that uses a parallel neural network to take a single fashion item shop image as input and make in-shop recommendations by listing similar items available in the store. Next, the same structure is enhanced to personalize the results based on user preferences. This work then introduces a background augmentation technique that makes the system more robust to out-of-domain queries, enabling it to make street-to-shop recommendations using only a training set of catalog shop images. Moreover, the last contribution of this paper is a new evaluation metric for recommendation tasks called objective-guided human score. This method is an entirely customizable framework that produces interpretable, comparable scores from subjective evaluations of human scorers.

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Seyed Omid Mohammadi, Hossein Bodaghi, Ahmad Kalhor. 2021-11-01. Single-Item Fashion Recommender: Towards Cross-Domain Recommendations. https://doi.org/10.1109/icee55646.2022.9827421

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