Spatial processes for recommender systems

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2009, Pages 2022-2027

  Bohnert, Fabian ^a   Schmidt, Daniel F ^a   Zukerman, Ingrid ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

MUSEUMS;

GEOGRAPHIC DATA; MODEL-BASED OPC; PREDICTIVE ACCURACY; REAL-WORLD; SPATIAL PROCESS; STATE OF THE ART; THEORETICAL FRAMEWORK; USER'S INTEREST;

RECOMMENDER SYSTEMS;

EID: 78751679438     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. D.W. Albrecht and I. Zukerman. Introduction to the special issue on statistical and probabilistic methods for user modeling. UMUAI, 17(1-2):1-4, 2007.
2. C. Andrieu, N. de Freitas, A. Doucet, and M.I. Jordan. An introduction to MCMC for machine learning. Machine Learning, 50(1):5-43, 2003.
3. S. Banerjee, B.P. Carlin, and A.E. Gelfand. Hierarchical Modeling and Analysis for Spatial Data. Chapman & Hall/CRC, 2004.
4. R. Bell, Y. Koren, and C. Volinsky. Chasing $1,000,000: How we won the Netflix progress prize. ASA Statistical and Computing Graphics Newsletter, 18(2):4-12, 2007.
5. F. Bohnert, I. Zukerman, S. Berkovsky, T. Baldwin, and L. Sonenberg. Using interest and transition models to predict visitor locations in museums. AI Communications, 21(2-3):195-202, 2008.
6. J.S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In UAI-98 Proc., pages 42-52, 1998.
7. R. Burke. Hybrid recommender systems: Survey and experiments. UMUAI, 12(4):331-370, 2002.
8. P.J. Diggle, J.A. Tawn, and R.A. Moyeed. Model-based geostatistics. Applied Statistics, 47(3):299-350, 1998.
9. J.L. Herlocker, J.A. Konstan, A. Borchers, and J.T. Riedl. An algorithmic framework for performing collaborative filtering. In SIGIR-99 Proc., pages 230-237, 1999.
10. W. James and C.M. Stein. Estimation with quadratic loss. In Proc. of the Fourth Berkeley Symp. on Mathematical Statistics and Probability, Vol. 1, pages 361-379, 1961.
11. R.M. Neal. Slice sampling. The Annals of Statistics, 31(3):705-767, 2003.
12. P. Resnick and H.R. Varian. Recommender systems. Communications of the ACM, 40(3):56-58, 1997.
13. B. Sarwar, G. Karypis, J.A. Konstan, and J.T. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW-01 Proc., pages 285-295, 2001.
14. A. Schwaighofer, V. Tresp, and K. Yu. Learning Gaussian process kernels via hierarchical Bayes. In NIPS-04 Proc., pages 1209-1216. 2005.
15. S. Yu, K. Yu, V. Tresp, and H.-P. Kriegel. Collaborative ordinal regression. In ICML-06 Proc., pages 1089-1096, 2006.