What is Matrix Factorization?
Matrix factorization is a class of collaborative filtering techniques used to predict user preferences for items (e.g., movies, products) by decomposing the user-item interaction matrix into two lower-dimensional matrices. The interaction matrix RRR can be approximated as the product of two matrices:
[Tex]R \approx U \cdot V^T[/Tex]
where:
- U is a matrix of user latent factors.
- V is a matrix of item latent factors.
Each user and item is represented by a vector in a latent factor space, capturing the underlying preferences and characteristics.
Probabilistic Matrix Factorization
Probabilistic Matrix Factorization (PMF) is a sophisticated technique in the realm of recommendation systems that leverages probability theory to uncover latent factors from user-item interaction data. PMF is particularly effective in scenarios where data is sparse, making it a powerful tool for delivering personalized recommendations.
This article explores the fundamentals of Probabilistic Matrix Factorization, its advantages, and how it is implemented in recommendation systems.
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