  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.
