Patterns for Personalization in Recommendations and Search

Eugene Yan · 5 years ago

This article explains patterns for personalization in recommendations and search systems, covering contextual bandits (which continuously learn through exploration and exploitation) and embedding-based deep learning approaches (which map features to vectors then process through neural networks). Netflix uses contextual bandits for image selection with a take-fraction metric of quality plays per impressions, while YouTube applies embedding pooling and mean-pooling strategies across millions of video candidates. These techniques allow systems to personalize experiences by learning user preferences through continuous adaptation or by compressing variable-length user histories into fixed-size vectors for ranking.