Visually Aware Skip-Gram for Image Based Recommendations
This addresses the cold-start problem in e-commerce recommendations, but it is incremental as it combines existing Skip-Gram and deep learning methods.
The paper tackled the problem of recommending cold-start products with no purchase history by proposing VASG, a framework that learns user and product representations in a common latent space using product image features, and experiments on real-world datasets showed it generates effective recommendations via nearest neighbor searches.
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.