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arXiv · 2008.06908

Visually Aware Skip-Gram for Image Based Recommendations

Parth Tiwari·Yash Jain·Shivansh Mundra·Jenny Harding·Manoj Kumar Tiwari
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Abstract

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.

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Parth Tiwari, Yash Jain, Shivansh Mundra, Jenny Harding, Manoj Kumar Tiwari. 2020-08-16. Visually Aware Skip-Gram for Image Based Recommendations. https://arxiv.org/abs/2008.06908

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