Context Aware Transformer

Context-Aware Transformer is a recommendation model that learns sequential user behavior from user-item interactions, timestamps, ratings, and text descriptions.

Text descriptions are ran through language models to get a latent space representation which will be used as more context for the model, this leads to a level if knowledge distillation from the Language Models into the final context aware model,

By incorporating these contextual features, the model achieves state of the art prediction accuracy, enabling personalized recommendations that improve user satisfaction and retention at large scale.

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