Create a Personalized Movie Recommendation Engine using Content-based Filtering in Python
Content-based recommender systems are a popular type of machine learning algorithm that recommends relevant articles based on what a user … Read more
Content-based Filtering
Here you’ll find everything about content-based filtering, including Python tutorials.
A content-based filter is a type of recommender system that uses the characteristics or content of items being recommended to make recommendations. This is in contrast to collaborative filtering, which uses the past behavior or preferences of users to make recommendations. Content-based filtering algorithms work by analyzing the content of each item. They aim to identify similar items that a user might be interested in. For example, imagine a user listening to a particular song or album on a music streaming service. The algorithm might recommend other songs by the same artist or songs with similar audio features (such as tempo or genre). In this way, the algorithm can provide personalized recommendations that consider the specific interests and preferences of each user.