A recommender system, also known as a recommendation system, is a type of software application or algorithm designed to suggest items or content to users based on their preferences and behaviors. Recommender systems are widely used in various industries, including e-commerce, streaming services, social media, and more. The primary goal is to enhance user experience by providing personalized and relevant recommendations. Here’s an overview of key concepts and techniques related to recommender systems:
I. Types of Recommender Systems:
1. Collaborative Filtering:
- Collaborative filtering relies on user-item interactions and similarities between users or items. There are two main types:
- User-Based Collaborative Filtering: Recommends items based on the preferences of users with similar tastes.
- Item-Based Collaborative Filtering: Recommends items similar to those the user has interacted with or liked.
2. Content-Based Filtering:
- Content-based filtering recommends items based on their features and the user’s preferences. It involves analyzing item attributes and user profiles to make personalized recommendations.
