PyTorch DataLoader for Recommendation System: Loading and Preparing User-Item Data

In this video, we dive into the data loading and preparation process for our recommendation system using PyTorch. We’ll walk through the creation of a custom DataLoader that efficiently loads user-item interaction data and prepares it for model training. Learn how to handle large datasets, split data into training and validation sets, and use PyTorch’s Dataset and DataLoader classes to feed data into your recommendation model. This is an essential step in building any machine learning application, and by the end of this video, you’ll be confident in how to manage and preprocess your data for a recommendation system.

What you'll learn:
Creating a custom PyTorch Dataset class for user-item interactions
Efficiently loading and batching data using PyTorch’s DataLoader
Splitting your dataset into training and validation sets
Preprocessing steps for user and item IDs in recommendation systems