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A latent factor model for highly multi-relational data
| Citations: | 31 - 4 self |
BibTeX
@MISC{Jenatton_alatent,
author = {Rodolphe Jenatton and Antoine Bordes and Nicolas Le Roux and Guillaume Obozinski},
title = {A latent factor model for highly multi-relational data},
year = {}
}
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Abstract
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 this paper, 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. 1
Keyphrases
multi-relational data latent factor model multiple type various order movie preference large body bilinear structure type grows standard tensor-factorization datasets social network many data nlp application large multi-relational datasets meaningful verb representation multiple relation latent factor different relation state-of-the-art result knowledge base
