Graphical models for relations: Modeling relational context

KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

Volumn , Issue , 2011, Pages 114-120

  Tresp, Volker ^a,b   Huang, Yi ^a   Jiang, Xueyan ^b   Rettinger, Achim ^c  

^a SIEMENS AG   (Germany)

^b UNIVERSITY OF MUNICH   (Germany)

^c KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

Author keywords

Graphical models; Probabilistic relational models; Recommendation systems; Relational context information; Relational learning

Indexed keywords

CONTEXT INFORMATION; CONTEXTUAL INFORMATION; CONTINGENCY TABLE; GENERATIVE MODEL; GRAPHICAL MODEL; GRAPHICAL REPRESENTATIONS; MODEL DEPENDENCIES; MULTINOMIAL MODEL; MULTINOMIALS; PROBABILISTIC RELATIONAL MODELS; RELATIONAL LEARNING; SAMPLING MODEL; SOCIAL NETWORKS;

INFORMATION RETRIEVAL; MOTION PICTURES; RECOMMENDER SYSTEMS;

GRAPHIC METHODS;

EID: 84862155978     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Bell, R.M., Koren, Y., and Volinsky, C. (2010). All together now: A perspective on the netflix prize. Chance.
2. Breese, J. S., Heckerman, D., and Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Uncertainty in Artificial Intelligence.
3. Cands, E. J. and Recht, B. (2008). Exact matrix completion via convex optimization. Computing Research Repository - CORR.
4. Chu, W. and Ghahramani, Z. (2009). Probabilistic models for incomplete multi-dimensional arrays. In AISTATS.
5. Chu, W., Sindhwani, V., Ghahramani, Z., and Keerthi, S. S. (2006). Relational learning with gaussian processes. In NIPS.
6. Domingos, P. and Richardson, M. (2007). Markov logic: A unifying framework for statistical relational learning. In Getoor, L. and Taskar, B., editors, Introduction to Statistical Relational Learning. MIT Press.
7. Getoor, L., Friedman, N., Koller, D., Pferrer, A., and Taskar, B. (2007). Probabilistic relational models. In Getoor, L. and Taskar, B., editors, Introduction to Statistical Relational Learning. MIT Press.
8. Heckerman, D., Chickering, D.M., Meek, C., Rounthwaite, R., and Kadie, C. M. (2000). Dependency networks for inference, collaborative filtering, and data visualization. Journal of Machine Learning Research.
9. Hofmann, T. (1999). Probabilistic latent semantic analysis. In Uncertainty in Artificial Intelligence (UAI).
10. Huang, Y., Tresp, V., Bundschus, M., Rettinger, A., and Kriegel, H.-P. (2010). Multivariate structured prediction for learning on the semantic web. In Proceedings of the 20th International Conference on Inductive Logic Programming (ILP).
11. Jarvelin, K. and Kekalainen, J. (2000). IR evaluation methods for retrieving highly relevant documents. In SIGIR' 00.
12. Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T., and Ueda, N. (2006). Learning systems of concepts with an infinite relational model. In Poceedings of the National Conference on Artificial Intelligence (AAAI).
13. Kolda, T. G. and Bader, B. W. (2009). Tensor decompositions and applications. SIAM Review.
14. Koller, D. and Pfeffer, A. (1998). Probabilistic frame-based systems. In Proceedings of the National Conference on Artificial Intelligence (AAAI).
15. Lauritzen, S. L. (1996). Graphical Models. Oxford Statistical Science Series.
16. Rendle, S., Freudenthaler, C., and Schmidt-Thieme, L. (2010). Factorizing personalized markov chains for next-basket recommendation. In World Wide Web Conference.
17. Salakhutdinov, R. andMnih, A. (2007). Probabilisticmatrix factorization. In NIPS.
18. Takacs, G., Pilaszy, I., Nemeth, B., and Tikk, D. (2007). On the gravity recommendation system. In Proceedings of KDD Cup and Workshop 2007.
19. Taskar, B., Abbeel, P., and Koller, D. (2002). Discriminative probabilistic models for relational data. In Uncertainty in Artificial Intelligence (UAI).
20. Wermser, H., Rettinger, A., and Tresp, V. (2011). Modeling and learning context-aware recommendation scenarios using tensor decomposition. In Proc. of the International Conference on Advances in Social Networks Analysis and Mining.
21. Xu, Z., Tresp, V., Yu, K., and Kriegel, H.-P. (2006). Infinite hidden relational models. In Uncertainty in Artificial Intelligence (UAI).
22. Yu, K., Chu, W., Yu, S., Tresp, V., and Xu, Z. (2006). Stochastic relational models for discriminative link prediction. In Advances in Neural Information Processing Systems 19.