A Boosting Framework of Factorization Machine

TL;DR

This paper introduces AdaFM, an adaptive boosting framework for Factorization Machines that automatically adjusts the rank during training, improving efficiency and performance on large-scale recommendation datasets.

Contribution

The paper proposes AdaFM, a novel boosting approach that adaptively determines the optimal rank for Factorization Machines without multiple re-training cycles.

Findings

AdaFM outperforms state-of-the-art FMs in experiments

Adaptive rank selection improves model efficiency

Empirical results demonstrate enhanced recommendation accuracy

Abstract

Recently, Factorization Machines (FM) has become more and more popular for recommendation systems, due to its effectiveness in finding informative interactions between features. Usually, the weights for the interactions is learnt as a low rank weight matrix, which is formulated as an inner product of two low rank matrices. This low rank can help improve the generalization ability of Factorization Machines. However, to choose the rank properly, it usually needs to run the algorithm for many times using different ranks, which clearly is inefficient for some large-scale datasets. To alleviate this issue, we propose an Adaptive Boosting framework of Factorization Machines (AdaFM), which can adaptively search for proper ranks for different datasets without re-training. Instead of using a fixed rank for FM, the proposed algorithm will adaptively gradually increases its rank according to its performance until the performance does not grow, using boosting strategy. To verify the performance of our proposed framework, we conduct an extensive set of experiments on many real-world datasets. Encouraging empirical results shows that the proposed algorithms are generally more effective than state-of-the-art other Factorization Machines.