Boosting Factorization Machines via Saliency-Guided Mixup. (arXiv:2206.08661v1 [cs.IR])

Web: http://arxiv.org/abs/2206.08661

Factorization machines (FMs) are widely used in recommender systems due to
their adaptability and ability to learn from sparse data. However, for the
ubiquitous non-interactive features in sparse data, existing FMs can only
estimate the parameters corresponding to these features via the inner product
of their embeddings. Undeniably, they cannot learn the direct interactions of
these features, which limits the model's expressive power. To this end, we
first present MixFM, inspired by Mixup, to generate auxiliary training data to
boost …

arxiv boosting factorization machines