Online experiments showed that "Wide & Deep" significantly increased app acquisitions compared to models that used either approach alone [1606.07792].
Discuss the used in the model (e.g., user, context, item features). 888.470760_415140.lt.
This architecture has since become a standard baseline for many recommendation tasks in industry, including those described in studies on YouTube recommendations [1606.07792]. If you'd like, I can: Online experiments showed that "Wide & Deep" significantly
The implementation was made publicly available within TensorFlow . 888.470760_415140.lt.
The model was heavily used for app recommendations on the Google Play Store [1606.07792].
The paper proposes training both components simultaneously rather than separately. This allows the model to optimize for both accuracy (via the wide component) and serendipity/novelty (via the deep component) [1606.07792]. Key Results & Impact