Wide & Deep
2024 | AI Dictionary
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.
Did you liked the Wide & Deep gist?
Learn about 250+ need-to-know artificial intelligence terms in the AI Dictionary.