GRAPHICAL MODELS FOR RELATIONS - Modeling Relational Context
Volker Tresp, Yi Huang, Xueyan Jiang, Achim Rettinger · 2011
We derive a multinomial sampling model for analyzing the relationships between two or more entities. The parameters in the multinomial model are derived from factorizing multi-way contingency tables. We show how contextual information can be included and propose a graphical representation of model dependencies. The graphical representation allows us to decompose a multivariate domain into interactions involving only a small number of variables. The approach formulates a probabilistic generative model for a single relation. By construction, the approach can easily deal with missing relations. We apply our approach to a social network domain where we predict the event that a user watches a movie. Our approach permits the integration of both information about the last movie watched by a user and a general temporal preference for a movie.