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.

Implementation of Genetic Algorithm for 0/1 Knapsack Problem in MATLAB

This MATLAB program solves the 0/1 knapsack problem using genetic algorithm optimization. The algorithm selects items from n available objects to pack into a knapsack with capacity c, where each item i has weight w_i and value p_i. The solution ensures the total weight doesn't exceed capacity while maximizing total value. The implementation includes key genetic operators: crossover, mutation, and selection with fitness evaluation.