A wide linear model is used, which excels at memorizing sparse feature interactions (e.g., user clicked 'item A' and user is from 'location B') [1606.07792].
Explain the in more detail (which also uses deep learning). Find the open-source code for the Wide & Deep model.
The model was heavily used for app recommendations on the Google Play Store [1606.07792]. 888.470760_415140.lt.
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). A wide linear model is used, which excels
The implementation was made publicly available within TensorFlow .
Recommender systems often struggle to balance memorization (learning frequent, specific co-occurrences of items/features) and generalization (recommending items that haven't explicitly appeared together in the training data) [1606.07792]. The model was heavily used for app recommendations
The query likely refers to the seminal 2016 paper published by researchers at Google [1606.07792]. This paper introduced a model that combines the strengths of linear models (memorization) and deep neural networks (generalization) to improve recommendation quality. Core Concepts of the "Wide & Deep" Paper