Factorization Machines have gained popularity in recent years due to their effectiveness in recommendation systems. FMs are general predictors which allow to capture interactions between all features in a features matrix. The feature matrices pertinent to the recommendation systems are highly sparse. SystemML's highly efficient distributed sparse matrix operations can be leveraged to implement FMs in a scalable fashion. Given the closed model equation of FMs, the model parameters can be learned using gradient descent methods.
Implementation of factorization machines, as described in the paper, as a core module to support
- Binary classification
We'll showcase the scalability of SystemML, with an end-to-end recommender system. Possibly, we could integrate some other algorithms to build a state-of-the-art recommender system.