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Title:Learning embeddings for fashion recommendation
Author(s):Rajpal, Shreya
Advisor(s):Forsyth, David A.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
fashion recommendation
computer vision
Abstract:In this work, we present a novel methodology to recommend items that are compatible with a given item of clothing. Compatibility is a hard notion to capture because of its diversity and subjectivity. We propose an embedding based approach to solve this problem, and perform recommendation based on product-closeness to the given clothing item. We perform this by first decomposing the notion of product-closeness into two inter-related notions of product similarity and product compatibility. Then, we incorporate product type into our embedding mechanism, and learn different embedding networks for different product types. We evaluate our proposed strategy extensively, and demonstrate that it performs better than the baseline, and is an effective method for performing few-shot transfer to compatibility prediction tasks.
Issue Date:2018-04-25
Rights Information:Copyright 2018 Shreya Rajpal
Date Available in IDEALS:2018-09-04
Date Deposited:2018-05

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