Scaling Factorization Machines to Relational Data
The most common approach in predictive modeling is to describe cases with feature vectors (aka design matrix). Many machine learning methods such as linear regression or support vector machines rely on this representation. However, when the underlying data has strong relational patterns, especially relations with high cardinality, the design matrix can get very large which can make learning and prediction slow or even infeasible. This paper solves this issue by making use of repeating patterns in the design matrix which stem from the underlying relational structure of the data.