Authors:
Volker Tresp
1
;
Yi Huang
2
;
Xueyan Jiang
3
and
Achim Rettinger
4
Affiliations:
1
Siemens AG, Corporate Technology and Ludwig-Maximilians-Universität München, Germany
;
2
Siemens AG and Corporate Technology, Germany
;
3
Ludwig-Maximilians-Universität München, Germany
;
4
Karlsruhe Institute of Technology, Germany
Keyword(s):
Relational learning, Probabilistic relational models, Relational context information, Recommendation systems, Graphical models.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaborative Filtering
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
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.