Simulated Annealing with Levy Distribution for Fast Matrix Factorization-Based Collaborative Filtering
This work addresses efficiency issues in collaborative filtering for recommendation systems, but it appears incremental as it builds on established matrix factorization techniques.
The paper tackles the complexity and parallelization challenges of matrix factorization for collaborative filtering by introducing a method combining simulated annealing with Levy distribution, achieving good solutions with low computations in acceptable time compared to existing methods like stochastic gradient descent.
Matrix factorization is one of the best approaches for collaborative filtering, because of its high accuracy in presenting users and items latent factors. The main disadvantages of matrix factorization are its complexity, and being very hard to be parallelized, specially with very large matrices. In this paper, we introduce a new method for collaborative filtering based on Matrix Factorization by combining simulated annealing with levy distribution. By using this method, good solutions are achieved in acceptable time with low computations, compared to other methods like stochastic gradient descent, alternating least squares, and weighted non-negative matrix factorization.