Online experiments showed that "Wide & Deep" significantly increased app acquisitions compared to models that used either approach alone [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 888.470760_415140.lt.
Discuss the used in the model (e.g., user, context, item features). Online experiments showed that "Wide & Deep" significantly
The implementation was made publicly available within TensorFlow . 888.470760_415140.lt.
Explain the in more detail (which also uses deep learning). Find the open-source code for the Wide & Deep model.