Optimizing multi-relational factorization models for multiple target relations

CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management

Volumn , Issue , 2014, Pages 191-200

  Drumond, Lucas ^a   Diaz Aviles, Ernesto ^b   Schmidt Thieme, Lars ^a   Nejdl, Wolfgang ^c  

^a UNIVERSITY OF HILDESHEIM   (Germany)

^b IBM RESEARCH   (Ireland)

^c LEIBNIZ UNIVERSITY HANNOVER   (Germany)

Author keywords

Factorization models; Relational learning; Statistical inference

Indexed keywords

FACTORIZATION; FORECASTING; KNOWLEDGE MANAGEMENT; LARGE DATASET; OPEN DATA; PREDICTIVE ANALYTICS;

AUXILIARY PARAMETERS; FACTORIZATION MODEL; LINKED OPEN DATA (LOD); MATRIX FACTORIZATIONS; PREDICTIVE PERFORMANCE; RELATIONAL LEARNING; STATE-OF-THE-ART METHODS; STATISTICAL INFERENCE;

LEARNING SYSTEMS;

EID: 84937548042     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2661829.2662052     Document Type: Conference Paper

References (22)

1. E. Acar, T. G. Kolda, and D. M. Dunlavy. All-at-once Optimization for Coupled Matrix and Tensor Factorizations. In MLG'11: Proceedings of Mining and Learning with Graphs, August 2011.
2. R. A. Baeza-Yates and B. A. Ribeiro-Neto. Modern Information Retrieval - the concepts and technology behind search, Second edition. Pearson Education Ltd., Harlow, England, 2011.
3. A. Bordes, X. Glorot, J. Weston, and Y. Bengio. A semantic matching energy function for learning with multi-relational data. Machine Learning, 94(2), 2014.
4. P. Cremonesi, Y. Koren, and R. Turrin. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, 2010.
5. E. Diaz-Aviles, L. Drumond, L. Schmidt-Thieme, and W. Nejdl. Real-time Top-n Recommendation in Social Streams. In Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys'12, 2012.
6. L. Drumond, S. Rendle, and L. Schmidt-Thieme. Predicting RDF triples in incomplete knowledge bases with tensor factorization. In Proceedings of the 27th Annual ACM Symposium on Applied Computing, SAC'12, 2012.
7. L. Getoor and B. Taskar, editors. Introduction to Statistical Relational Learning. The MIT Press, 2007.
8. R. Harshman. Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-mode factor. In UCLA Working Papers in Phonetics, 1970.
9. R. Jenatton, N. L. Roux, A. Bordes, and G. Obozinski. A latent factor model for highly multi-relational data. In Advances in Neural Information Processing Systems (NIPS). 2012.
10. A. Krohn-Grimberghe, L. Drumond, C. Freudenthaler, and L. Schmidt-Thieme. Multi-relational matrix factorization using bayesian personalized ranking for social network data. In Proceedings of the fifth ACM International Conference on Web Search and Data Mining WSDM'12, 2012.
11. Y.-R. Lin, J. Sun, P. Castro, R. Konuru, H. Sundaram, and A. Kelliher. MetaFac: Community Discovery via Relational Hypergraph Factorization. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'09, 2009.
12. H. Ma, H. Yang, M. R. Lyu, and I. King. SoRec: social recommendation using probabilistic matrix factorization. In Proceeding of the 17th ACM conference on Information and knowledge management, CIKM'08, 2008.
13. M. Nickel, V. Tresp, and H. Kriegel. A Three-Way Model for Collective Learning on Multi-Relational Data. In Proceedings of the 2011 International Conference on Machine Learning (ICML), 2011.
14. M. Nickel, V. Tresp, and H.-P. Kriegel. Factorizing YAGO: scalable machine learning for linked data. In Proceedings of the 21st international conference on World Wide Web, WWW'12, 2012.
15. S. Rendle. Learning recommender systems with adaptive regularization. In Proceedings of the fifth ACM international conference on Web search and data mining, WSDM'12, 2012.
16. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI'09, 2009.
17. M. Richardson and P. Domingos. Markov logic networks. Machine learning, 62(1), 2006.
18. W. Shen, J. Wang, P. Luo, and M. Wang. LINDEN: linking named entities with knowledge base via semantic knowledge. In Proceedings of the 21st international conference on World Wide Web, WWW'12, 2012.
19. A. P. Singh and G. J. Gordon. Relational learning via collective matrix factorization. In Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008.
20. A. P. Singh and G. J. Gordon. A Bayesian matrix factorization model for relational data. In Proceedings of the Annual Conference on Uncertainty in Artificial Intelligence (UAI), 2010.
21. L. Tang and H. Liu. Relational learning via latent social dimensions. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD'09, 2009.
22. Y. Zhang, B. Cao, and D.-Y. Yeung. Multi-Domain Collaborative Filtering. In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI), 2010.