07_recommender_systems

Recommender Systems

This directory contains examples of recommender systems, which suggest items to users based on their preferences and behavior.

There are two main approaches:

1. Collaborative Filtering: Recommends items by comparing users and finding those with similar preferences.

2. Content-Based Filtering: Recommends items by matching item features (e.g., genre, ratings) to user features (e.g., past interactions, age).

Projects

• 01_collaborative_filtering
  • Collaborative filtering approach using user–item interaction data.
  • TensorFlow-based implementation.
• 02_content_based_filtering
  • Content-based filtering approach using explicit user and item features.
  • TensorFlow-based implementation.