Improve ranking algorithms based on matrix factorization in rating systems

Abstract

The proliferation of the Internet has led to an increase in the usage of rating systems. Inspired by matrix factorization, we present two improved iterative ranking algorithms called L1-AVG-RMF and AA-RMF for rating systems. In the new algorithms, the missing ratings are estimated by matrix factorization before applying traditional ranking algorithms. Theoretical analysis illustrates that the proposed algorithms have a better accuracy and robustness. And it is also demonstrated by Experiments with synthetic and real data. Additionally, experimental results also show that L1-AVG-RMF has superior effectiveness and robustness compared to some other ranking algorithms. Our findings emphasize the potential benefits of applying matrix factorization to ranking algorithms.