Algorithmic clothing: hybrid recommendation, from street-style-to-shop
This work addresses the problem of clothing recommendation for fashion consumers, but it is incremental as it builds on existing methods for segmentation and deep learning.
The paper tackles the problem of clothing recommendation by developing a hybrid visual recommender system that combines conditional random fields for segmentation and deep neural networks for object localization and feature representation, resulting in a system that is both knowledge-based and content-based, particularly useful in scenarios with scarce user preference data.
In this paper we detail Cortexica's (https://www.cortexica.com) recommendation framework -- particularly, we describe how a hybrid visual recommender system can be created by combining conditional random fields for segmentation and deep neural networks for object localisation and feature representation. The recommendation system that is built after localisation, segmentation and classification has two properties -- first, it is knowledge based in the sense that it learns pairwise preference/occurrence matrix by utilising knowledge from experts (images from fashion blogs) and second, it is content-based as it utilises a deep learning based framework for learning feature representation. Such a construct is especially useful when there is a scarcity of user preference data, that forms the foundation of many collaborative recommendation algorithms.