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.
@article{arxiv.2008.06908,
title = {Visually Aware Skip-Gram for Image Based Recommendations},
author = {Parth Tiwari and Yash Jain and Shivansh Mundra and Jenny Harding and Manoj Kumar Tiwari},
journal= {arXiv preprint arXiv:2008.06908},
year = {2020}
}