Relation prediction in multi-relational domains using matrix factorization

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Item Type:	Article
Title:	Relation prediction in multi-relational domains using matrix factorization
Creators Name:	Lippert, C., Weber, S.H., Huang, Y., Tresp, V., Schubert, M. and Kriegel, H.P.
Abstract:	The paper is concerned with relation prediction in multi-relational domains using matrix factorization. While most past predictive models focussed on one single relation type between two entity types, in the paper a generalized model is presented that is able to deal with an arbitrary number of relation types and entity types in a domain of interest. The novel multi-relational matrix factorization is domain independent and highly scalable. We validate the performance of our approach using two real-world data sets, i.e. user-movie recommendations and gene function prediction.
Keywords:	Relational-Learning, Relation Prediction, Matrix Factorization, Collaborative Filtering, Bioinformatics
Source:	Proceedings of the NIPS 2008 Workshop
Date:	2008

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