Matrix Factorization-Based Recommendation
Matrix factorization essentially posits that each user and item possesses latent characteristics. By decomposing the rating matrix into user-characteristic and characteristic-item matrices, this approach achieves two key benefits: it uncovers user preferences and item attributes while reducing matrix dimensionality for computational efficiency. Implementation typically involves optimization algorithms like stochastic gradient descent or alternating least squares to minimize the reconstruction error between the original and factorized matrices.