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Factorization machines (FM) are a popular model class to learn pairwiseinteractions by a low-rank approximation. Different from existing FM-basedapproaches which use a fixed rank for all features, this paper proposes aRank-Aware FM (RaFM) model which adopts pairwise interactions from embeddingswith different ranks. The proposed model achieves a better performance onreal-world datasets where different features have significantly varyingfrequencies of occurrences. Moreover, we prove that the RaFM model can bestored, evaluated, and trained as efficiently as one single FM, and under somereasonable conditions it can be even significantly more efficient than FM. RaFMimproves the performance of FMs in both regression tasks and classificationtasks while incurring less computational burden, therefore also has attractivepotential in industrial applications.

RaFM: Rank-Aware Factorization Machines