Wide&Deep: Combining Memorization and Generalization for Recommender Systems
Wide&Deep is a method used to train wide linear models and deep neural networks. The method combines the benefits of memorization and generalization for real-world recommender systems.
Before we dive into how Wide&Deep works, let's define some terms. Recommender systems are algorithms used to predict what a user might like based on their past behavior. A wide linear model is a type of machine learning model that can learn patterns from a large number of input features. A feed-forward neural network is a type of machine learning model that can learn patterns from a large number of hidden layers.
Memorization vs. Generalization
Memorization and generalization are two techniques used in machine learning. Memorization occurs when a model memorizes the patterns in the training data instead of learning how to generalize these patterns to new data. Generalization occurs when a model successfully identifies and models the underlying patterns in the training data and can apply these patterns to new data. A successful recommender system needs to balance memorization and generalization to provide accurate predictions.
The Wide Component
The wide component of Wide&Deep is a generalized linear model. This model can learn patterns from a large number of one-hot encoded input features. For example, if we are building a music recommender system, the wide component might include features like the artist, genre, and release year of songs. The wide component is called "wide" because it can learn from a wide range of input features. The wide component specializes in memorization because it can learn patterns from a large number of input features.
The Deep Component
The deep component of Wide&Deep is a feed-forward neural network. This model can learn patterns from a large number of hidden layers. For example, if we are building a music recommender system, the deep component might learn patterns from a user's listening history. The deep component is called "deep" because it can learn patterns from a deep network of hidden layers. The deep component specializes in generalization because it can identify and model the underlying patterns in the training data.
Combining the Wide and Deep Components
The wide and deep components of Wide&Deep are combined using a weighted sum of their output log odds as the prediction. The output is then fed to a logistic loss function for joint training. The optimization algorithm used for the wide component is called AdaGrad, which is a gradient descent algorithm that adjusts the learning rate for each parameter individually based on its past gradient history. The combined model is illustrated below:
Training the Model
The Wide&Deep model is trained using mini-batch stochastic optimization. This means that the model is trained on small randomly selected subsets of the training data instead of the entire dataset at once. The gradients are back-propagated from the output to both the wide and deep parts of the model simultaneously during training. This helps the model learn the right balance of memorization and generalization.
Benefits of Wide&Deep
The Wide&Deep method has several benefits for recommender systems. First, it provides a good balance of memorization and generalization. This means that the model can learn patterns from a large number of input features and hidden layers while also identifying and modeling the underlying patterns in the training data. Second, the method is easy to implement and can scale to large datasets. Third, the method has achieved state-of-the-art performance on several real-world datasets.
Wide&Deep is a method used to train wide linear models and deep neural networks that combine the benefits of memorization and generalization for real-world recommender systems. The method balances the strengths of both models to provide accurate predictions. The method has several benefits, including a good balance of memorization and generalization, scalability, and state-of-the-art performance.
