Alexander Tuzhilin - Google Scholar Citations

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Gediminas AdomaviciusProfessor of Information and Decision Sciences, University of MinnesotaVerified email at umn.edu
Balaji PadmanabhanVerified email at usf.edu
A SilberschatzProfessor of Computer Science at Yale UniversityVerified email at cs.yale.edu
Umberto PannielloPolitecnico di BariVerified email at poliba.it
Panagiotis (Panos) AdamopoulosGoizueta Business School, Emory UniversityVerified email at stern.nyu.edu
Yong GeUniversity of ArizonaVerified email at email.arizona.edu
Konstantin BaumanAssistant Professor, Fox School of Business, Temple UniversityVerified email at temple.edu
Bing LiuDistinguished Professor, University of Illinois at ChicagoVerified email at uic.edu
Hui XiongProfessor of Data Mining, Rutgers UniversityVerified email at rutgers.edu
Akhmed UmyarovUniversity of Minnesota. Carlson School of Management.Verified email at umn.edu
Michael J. PazzaniProfessor of Computer Science and Engineering, University of California, RiversideVerified email at UCR.edu
Ke WangProfessor of Computing Science, Simon Fraser UniversityVerified email at cs.sfu.ca
Markus ZankerFree University of Bolzano-BozenVerified email at unibz.it
Bamshad MobasherSchool of Computing, DePaul UniversityVerified email at cs.depaul.edu
Dietmar JannachUniversity of Klagenfurt, AustriaVerified email at aau.at
Paul ResnickUniversity of MichiganVerified email at umich.edu
Rong ZhengProfessor of Information System, HKUSTVerified email at ust.hk
Ada Wai-Chee FuChinese University of Hong KongVerified email at cse.cuhk.edu.hk
Ramesh ShankarAssociate Professor of Information Systems, University of ConnecticutVerified email at business.uconn.edu

Alexander Tuzhilin

Professor of Information Systems, NYU

Verified email at stern.nyu.edu

data mining machine learning personalization recommender systems information systems


Title	Cited by	Year
Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions G Adomavicius, A Tuzhilin IEEE Transactions on Knowledge & Data Engineering, 734-749, 2005	10778	2005
Context-aware recommender systems G Adomavicius, A Tuzhilin Recommender systems handbook, 217-253, 2011	2149	2011
Incorporating contextual information in recommender systems using a multidimensional approach G Adomavicius, R Sankaranarayanan, S Sen, A Tuzhilin ACM Transactions on Information Systems (TOIS) 23 (1), 103-145, 2005	1381	2005
System and method for dynamic profiling of users in one-to-one applications AS Tuzhilin US Patent 6,236,978, 2001	960	2001
What makes patterns interesting in knowledge discovery systems A Silberschatz, A Tuzhilin IEEE Transactions on Knowledge and data engineering 8 (6), 970-974, 1996	958	1996
10 challenging problems in data mining research Q Yang, X Wu International Journal of Information Technology & Decision Making 5 (04 …, 2006	696	2006
Architectures, systems, apparatus, methods, and computer-readable medium for providing recommendations to users and applications using multidimensional data A Tuzhilin, G Adomavicius US Patent 8,103,611, 2012	561*	2012
On subjective measures of interestingness in knowledge discovery. A Silberschatz, A Tuzhilin KDD 95, 275-281, 1995	538	1995
Personalization technologies: a process-oriented perspective G Adomavicius, A Tuzhilin Communications of the ACM 48 (10), 83-90, 2005	427	2005
Using data mining methods to build customer profiles G Adomavicius, A Tuzhilin Computer 34 (2), 74-82, 2001	371	2001
A Belief-Driven Method for Discovering Unexpected Patterns. B Padmanabhan, A Tuzhilin KDD 98, 94-100, 1998	362	1998
An energy-efficient mobile recommender system Y Ge, H Xiong, A Tuzhilin, K Xiao, M Gruteser, M Pazzani Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010	310	2010
The long tail of recommender systems and how to leverage it YJ Park, A Tuzhilin Proceedings of the 2008 ACM conference on Recommender systems, 11-18, 2008	303	2008
Unexpectedness as a measure of interestingness in knowledge discovery B Padmanabhan, A Tuzhilin Decision Support Systems 27 (3), 303-318, 1999	262	1999
Using context to improve predictive modeling of customers in personalization applications C Palmisano, A Tuzhilin, M Gorgoglione IEEE transactions on knowledge and data engineering 20 (11), 1535-1549, 2008	246	2008
Expert-driven validation of rule-based user models in personalization applications G Adomavicius, A Tuzhilin Data Mining and Knowledge Discovery 5 (1-2), 33-58, 2001	237	2001
Experimental comparison of pre-vs. post-filtering approaches in context-aware recommender systems U Panniello, A Tuzhilin, M Gorgoglione, C Palmisano, A Pedone Proceedings of the third ACM conference on Recommender systems, 265-268, 2009	216	2009
User profiling in personalization applications through rule discovery and validation G Adomavicius, A Tuzhilin Proceedings of the fifth ACM SIGKDD international conference on Knowledge …, 1999	195	1999
On unexpectedness in recommender systems: Or how to better expect the unexpected P Adamopoulos, A Tuzhilin ACM Transactions on Intelligent Systems and Technology (TIST) 5 (4), 54, 2015	192	2015
Small is beautiful: discovering the minimal set of unexpected patterns B Padmanabhan, A Tuzhilin Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000	189	2000

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