What are the recommendation system modules in Torch?
In Torch, recommender system modules are typically built as model classes using torch.nn.Module, commonly including modules such as:
- Embedding layer: used to map discrete features of the input to dense vector representations, commonly used for representing features of users and items.
- Neural network models, such as fully connected layers, convolutional layers, and recurrent neural networks, are used to learn the interactive relationships between users and items.
- Loss function: A tool used to measure the difference between a model’s predicted results and the actual labels, commonly used loss functions include mean squared error (MSE) and cross-entropy loss.
- Optimizer: Used to update model parameters, common optimizers include Stochastic Gradient Descent (SGD), Adam, etc.
- Data processing module: used for loading and preprocessing data, including data loaders and data preprocessing functions.
These modules can be combined to build different types of recommendation system models, such as collaborative filtering-based recommendation systems, deep learning-based recommendation systems, etc. By combining and adjusting these modules, recommendation system models suitable for different scenarios and tasks can be constructed.
