Section: New Results
A latent factor model for highly multi-relational data
Participants : Nicolas Le Roux, Guillaume Obozinski [correspondant] .
Many data such as social networks, movie preferences or knowledge bases are multi-relational, in that they describe multiple relations between entities. While there is a large body of work focused on modeling these data, modeling these multiple types of relations jointly remains challenging. Further, existing approaches tend to breakdown when the number of these types grows. In  , we propose a method for modeling large multi relational datasets, with possibly thousands of relations. Our model is based on a bilinear structure, which captures various orders of interaction of the data, and also shares sparse latent factors across different relations. We illustrate the performance of our approach on standard tensor-factorization datasets where we attain, or outperform, state-of-the-art results. Finally, a NLP application demonstrates our scalability and the ability of our model to learn efficient and semantically meaningful verb representations.
Collaboration with R. Jenatton (CMAP, Ecole Polytechnique) and Antoine Bordes (CNRS, Université de Technologie de Compiégne).