Machine learning
Reading recommendation system
Neural collaborative filtering over reading interactions, using user and item embeddings with a multilayer prediction network.
Overview
Learning what a reader might enjoy next
This experiment explores reading recommendations using patterns in user interactions. Rather than comparing content only by keywords, it learns relationships between readers and the items they engage with.
The goal is to turn interaction history into a preference signal that could help order reading suggestions. The current project is a model experiment, rather than a deployed recommendation product.
Technical detail
Preprocessing maps users and items to numerical indices and prepares ratings for a custom PyTorch Dataset. The model learns separate user and item embeddings, concatenates them, and passes them through a multilayer network to predict an interaction score.
Reusable dataset and model modules are separated from the experiment notebook. This makes the data representation and scoring architecture easier to examine independently of the exploratory workflow.