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Single-Item Fashion Recommender: Towards Cross-Domain Recommendations

2021-11-01 08:15:31
Seyed Omid Mohammadi, Hossein Bodaghi, Ahmad Kalhor (University of Tehran, College of Engineering, School of Electrical and Computer Engineering, Tehran, Iran)

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.

Abstract (translated)

URL

https://arxiv.org/abs/2111.00758

PDF

https://arxiv.org/pdf/2111.00758.pdf


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