Email this article Micro-blogging attributes to Latent Feature Representation for Product Recommendations
Abstract
We suggest to use the linked users through social networking sites and e-commerce websites as a link to map users’ social networking structures to added feature demonstration for product recommendation. In detailed, we suggest wisdom both users’ and products’ feature illustrations (called user embeddings and product embeddings, individually) from data collected from e-commerce websites using repeated neural networks and then apply a improved gradient boosting trees technique to change users’ social networking structures into user embeddings. We then improve a feature-based matrix factorization method which can leverage the learnt user embeddings for cold-start product recommendation.
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