  The visual appearance of a product significantly influences purchase
decisions on e-commerce websites. We propose a novel framework VASG (Visually
Aware Skip-Gram) for learning user and product representations in a common
latent space using product image features. Our model is an amalgamation of the
Skip-Gram architecture and a deep neural network based Decoder. Here the
Skip-Gram attempts to capture user preference by optimizing user-product
co-occurrence in a Heterogeneous Information Network while the Decoder
simultaneously learns a mapping to transform product image features to the
Skip-Gram embedding space. This architecture is jointly optimized in an
end-to-end, multitask fashion. The proposed framework enables us to make
personalized recommendations for cold-start products which have no purchase
history. Experiments conducted on large real-world datasets show that the
learned embeddings can generate effective recommendations using nearest
neighbour searches.
