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Matrix factorization is a technique used in recommender systems to predict user preferences or ratings for items based on the latent features of users and items. The basic idea is to decompose the user-item interaction matrix into two lower-dimensional matrices, capturing the underlying patterns or latent factors. This allows for efficient representation and reconstruction of the original matrix, making recommendations for unseen items.
Here’s an overview of matrix factorization in recommender systems:
1. User-Item Interaction Matrix:
- In a recommender system, the user-item interaction matrix represents the historical interactions (e.g., ratings) between users and items. The matrix is typically sparse because users don’t interact with all items.
2. Matrix Factorization:
- Matrix factorization decomposes the user-item interaction matrix (R) into two lower-dimensional matrices: one for users (U) and one for items (V).
- R≈U×VT
- Each row of the U matrix corresponds to a user, and each row of the V matrix corresponds to an item. The dot product of a user’s row in U and an item’s row in V yields the predicted rating.
