What Wide & Deep Meaning, Applications & Example
Neural network architecture combining memorization and generalization.
What is Wide & Deep?
Wide & Deep is a model architecture that combines two different approaches to learning: a wide linear model and a deep neural network . The goal is to leverage the strengths of both. The wide part captures memorization of features and relationships, while the deep part captures patterns through generalization.
Components of Wide & Deep
Wide Component: A linear model that memorizes interactions between features. It is effective for handling categorical data or scenarios where memorization of specific patterns is important.
Deep Component: A neural network that learns complex feature representations. It helps the model generalize to unseen data by learning hierarchical feature representations.
Applications of Wide & Deep
- Recommendation Systems: Combining both memorization (wide) and generalization (deep) is useful for recommending items based on both specific user-item interactions and generalized patterns.
- Search Ranking: Improves search result ranking by using both personalized features (wide) and broader patterns (deep).
- Ad Click-Through Prediction: Combines historical data (wide) and general patterns of user behavior (deep) to predict ad clicks.
Example of Wide & Deep
In a recommendation system, the wide component might memorize the direct interaction between a user and an item (e.g., “User A liked Item B”), while the deep component might generalize from the user’s broader preferences and demographic features. By combining both, the model can improve recommendation accuracy.