Content-based filtering algorithm in social media

Abstract

Content-based filtering is a recommendation algorithm that analyzes user activity and profile data to provide personalized recommendations for content that matches a user's interests and preferences. This algorithm is widely used by social media platforms, such as Facebook and Twitter, to increase user engagement and satisfaction. The methodology of content-based filtering involves creating a user profile based on user activity and recommending content that matches the user's interests. The algorithm continually updates and personalizes the recommendations based on user feedback, and incorporates strategies to promote diversity and serendipity in the recommendations. While content-based filtering has some limitations, it remains a powerful tool in the arsenal of social media platforms, offering efficient content discovery and personalized user experiences at scale.

Keywords

• content-based
• machine learning
• deep learning