Matrix Factorization
Matrix Factorization is a mathematical technique used to break down a large matrix into smaller, more manageable matrices. This process helps to uncover hidden patterns and relationships within the data. For example, in recommendation systems like those used by Netflix or Spotify, matrix factorization can identify user preferences and suggest content that aligns with their tastes.
By representing users and items in a lower-dimensional space, matrix factorization simplifies complex data interactions. It allows systems to predict how much a user might like an item they haven't yet encountered. This approach enhances user experience by providing personalized recommendations based on learned patterns from existing data.