Optimizing Multi-Relational Factorization Models for Multiple Target Relations

Multi-matrix factorization models provide a scalable and effective approach for multi-relational learning tasks such as link prediction, Linked Open Data (LOD) mining, recommender systems and social network analysis. Such models are learned by optimizing the sum of the losses on all relations in the data. Early models address the problem where there is only one target relation for which predictions should be made. More recent models address the multi-target variant of the problem and use the same set of parameters to make predictions for all target relations. In this paper, we argue that a model optimized for each target relation individually has better predictive performance than models optimized for a compromise on the performance on all target relations. We introduce specific parameters for each target but, instead of learning them independently from each other, we couple them through a set of shared auxiliary parameters, which has a regularizing effect on the target specific ones. Experiments on large Web datasets derived from DBpedia, Wikipedia and BlogCatalog show the performance improvement obtained by using target specific parameters and that our approach outperforms competitive state-of-the-art methods while being able to scale gracefully to big data.