Simulated Annealing with Levy Distribution for Fast Matrix Factorization-Based Collaborative Filtering

TL;DR

This paper introduces a novel matrix factorization method for collaborative filtering that combines simulated annealing with Levy distribution, achieving faster solutions with lower computational costs compared to existing techniques.

Contribution

The paper presents a new approach integrating simulated annealing and Levy distribution for matrix factorization, improving speed and efficiency in collaborative filtering.

Findings

Achieves good solutions in acceptable time

Reduces computational complexity

Outperforms stochastic gradient descent and ALS

Abstract

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.