Improving Large-scale Recommendation Systems with Contextual Signals
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A recommendation system is an information filtering system to predict users' rating to items. Large-scale recommendation systems typically involve more than one billion users and items. Collaborative filtering based recommendation systems which were commonly used until recently suffer from the cold-start problem and the scalability problem making them unsuitable for large-scale recommendation systems. Model-based recommendation systems have the advantage of better performance and scalability in large-scale systems. Contextual signals, such as user browsing histories, check-ins, latest posts, page views, etc., provide great potential to improve model-based recommendation systems.