Michael J. Pazzani

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Eamonn KeoghDistinguished Professor of Computer Science, University of California - RiversideVerified email at cs.ucr.edu
Selina ChuResearch Scientist, Nasa Jet Propulsion Laboratory, California Institute of TechnologyVerified email at jpl.nasa.gov
Pedro DomingosProfessor of Computer Science and Engineering, University of WashingtonVerified email at cs.washington.edu
Chris MerzMastercardVerified email at mastercard.com
Subramani ManiAssociate Professor of Medicine, University of New MexicoVerified email at salud.unm.edu
William ShankleUC Irvine Cognitive ScienceVerified email at shankleclinic.com
Sharad MehrotraDistinguished Professor of Computer Science, Univ. of California, IrvineVerified email at ics.uci.edu
Kamal AliSimplifaiVerified email at simplifai.ai
Padhraic SmythDistinguished Professor, Computer Science, University of California IrvineVerified email at ics.uci.edu
Douglas FisherVanderbilt UniversityVerified email at vanderbilt.edu
Geoff WebbProfessor, Department of Data Science and Artificial Intelligence, Monash UniversityVerified email at monash.edu
Pat LangleyDirector, ISLE; Research Scientist, Stanford UniversityVerified email at stanford.edu
Mark AckermanUniversity of Michigan, Ann ArborVerified email at umich.edu
Kamran AlipourApple Inc.Verified email at apple.com
Chelsea DobbinsThe University of QueenslandVerified email at uq.edu.au
Matt JonesProfessor of Computer Science, Computational Foundry, Swansea UniversityVerified email at swansea.ac.uk
George BuchananDeputy Dean of Research, School of Computing Technologies, RMIT UniversityVerified email at rmit.edu.au
Harold ThimblebyProfessor of Computer Science, Swansea UniversityVerified email at swansea.ac.uk
Gary MarsdenProfessor of Computer Science, University of Cape TownVerified email at cs.uct.ac.za
Keli XiaoStony Brook UniversityVerified email at stonybrook.edu

Michael J. Pazzani

Distinguished Principal Scientist

Verified email at isi.edu - Homepage

Machine Learning Explainable Artificial Intelligence Personalization Recommender Systems Search


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
On the optimality of the simple Bayesian classifier under zero-one loss P Domingos, M Pazzani Machine learning 29 (2-3), 103-130, 1997	4674	1997
Content-based recommendation systems MJ Pazzani, D Billsus The adaptive web: methods and strategies of web personalization, 325-341, 2007	4032	2007
A framework for collaborative, content-based and demographic filtering MJ Pazzani Artificial intelligence review 13, 393-408, 1999	2466	1999
Dimensionality reduction for fast similarity search in large time series databases E Keogh, K Chakrabarti, M Pazzani, S Mehrotra Knowledge and information Systems 3, 263-286, 2001	2215	2001
Learning and revising user profiles: The identification of interesting web sites M Pazzani, D Billsus Machine learning 27, 313-331, 1997	2068	1997
Learning collaborative information filters. D Billsus, MJ Pazzani Icml 98, 46-54, 1998	1916	1998
An online algorithm for segmenting time series E Keogh, S Chu, D Hart, M Pazzani Proceedings 2001 IEEE international conference on data mining, 289-296, 2001	1700	2001
Syskill & Webert: Identifying interesting web sites MJ Pazzani, J Muramatsu, D Billsus AAAI/IAAI, Vol. 1, 54-61, 1996	1258	1996
Locally adaptive dimensionality reduction for indexing large time series databases E Keogh, K Chakrabarti, M Pazzani, S Mehrotra Proceedings of the 2001 ACM SIGMOD international conference on Management of …, 2001	1253	2001
Beyond independence: Conditions for the optimality of the simple bayesian classi er P Domingos, M Pazzani Proc. 13th Intl. Conf. Machine Learning, 105-112, 1996	1235	1996
Scaling up dynamic time warping for datamining applications EJ Keogh, MJ Pazzani Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000	1127	2000
Segmenting time series: A survey and novel approach E Keogh, S Chu, D Hart, M Pazzani Data mining in time series databases, 1-21, 2004	948	2004
User modeling for adaptive news access D Billsus, MJ Pazzani User modeling and user-adapted interaction 10, 147-180, 2000	930	2000
An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. EJ Keogh, MJ Pazzani Kdd 98, 239-243, 1998	845	1998
A hybrid user model for news story classification D Billsus, MJ Pazzani UM99 User Modeling: Proceedings of the Seventh International Conference, 99-108, 1999	683	1999
Machine learning for user modeling GI Webb, MJ Pazzani, D Billsus User modeling and user-adapted interaction 11, 19-29, 2001	642	2001
Locally adaptive dimensionality reduction for indexing large time series databases K Chakrabarti, E Keogh, S Mehrotra, M Pazzani ACM Transactions on Database Systems (TODS) 27 (2), 188-228, 2002	576	2002
Detecting group differences: Mining contrast sets SD Bay, MJ Pazzani Data mining and knowledge discovery 5, 213-246, 2001	551	2001
Reducing misclassification costs M Pazzani, C Merz, P Murphy, K Ali, T Hume, C Brunk Machine Learning Proceedings 1994, 217-225, 1994	533	1994
The utility of knowledge in inductive learning M Pazzani, D Kibler Machine learning 9, 57-94, 1992	505	1992

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