Content-Based Filtering

Content-Based Filtering is a recommendation system technique that suggests items to users based on the features of the items and the preferences exhibited by the user. It analyzes the attributes of the content consumed by the user, such as keywords, genres, or specific characteristics, to provide personalized recommendations. This method is particularly effective in scenarios where user preferences can be clearly defined through the content itself, such as in music, movie, or article recommendations. Unlike collaborative filtering, which relies on user interactions, content-based filtering focuses solely on the content attributes, making it useful even when user interaction data is sparse.