-
A Boosting Framework of Factorization Machine
Recently, Factorization Machines (FM) has become more and more popular f...
read it
-
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
Polynomial networks and factorization machines are two recently-proposed...
read it
-
DS-FACTO: Doubly Separable Factorization Machines
Factorization Machines (FM) are powerful class of models that incorporat...
read it
-
Low-Rank Factorization of Determinantal Point Processes for Recommendation
Determinantal point processes (DPPs) have garnered attention as an elega...
read it
-
Multi-output Polynomial Networks and Factorization Machines
Factorization machines and polynomial networks are supervised polynomial...
read it
-
Sparse Least Squares Low Rank Kernel Machines
A general framework of least squares support vector machine with low ran...
read it
-
ARSM Gradient Estimator for Supervised Learning to Rank
We propose a new model for supervised learning to rank. In our model, th...
read it
RaFM: Rank-Aware Factorization Machines
Factorization machines (FM) are a popular model class to learn pairwise interactions by a low-rank approximation. Different from existing FM-based approaches which use a fixed rank for all features, this paper proposes a Rank-Aware FM (RaFM) model which adopts pairwise interactions from embeddings with different ranks. The proposed model achieves a better performance on real-world datasets where different features have significantly varying frequencies of occurrences. Moreover, we prove that the RaFM model can be stored, evaluated, and trained as efficiently as one single FM, and under some reasonable conditions it can be even significantly more efficient than FM. RaFM improves the performance of FMs in both regression tasks and classification tasks while incurring less computational burden, therefore also has attractive potential in industrial applications.
READ FULL TEXT
Comments
There are no comments yet.