Alexander Tuzhilin

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Gediminas AdomaviciusProfessor of Information and Decision Sciences, University of MinnesotaVerified email at umn.edu
Balaji PadmanabhanAnderson Professor of Global Management at University of South FloridaVerified 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
Konstantin BaumanAssistant Professor, Fox School of Business, Temple UniversityVerified email at temple.edu
Hui Xiong, Fellow of AAAS and IEEEDistinguished Professor, Rutgers UniversityVerified email at rutgers.edu
Yong GeUniversity of ArizonaVerified email at email.arizona.edu
Bing LiuDistinguished Professor, University of Illinois at ChicagoVerified email at uic.edu
Akhmed UmyarovUniversity of Minnesota. Carlson School of Management.Verified email at umn.edu
Michael J. PazzaniDistinguished ScientistVerified email at UCSD.edu
Bamshad MobasherSchool of Computing, DePaul UniversityVerified email at cs.depaul.edu
Dietmar JannachUniversity of Klagenfurt, Austria; University of Bergen, NorwayVerified email at aau.at
Markus ZankerFree University of Bolzano-BozenVerified email at unibz.it
Ke WangProfessor of Computing Science, Simon Fraser UniversityVerified email at cs.sfu.ca
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 Sort by citations Sort by year Sort by title	Cited by 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 and data engineering 17 (6), 734-749, 2005	12407	2005
Context-aware recommender systems G Adomavicius, A Tuzhilin Recommender systems handbook, 217-253, 2011	2572	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	1555	2005
System and method for dynamic profiling of users in one-to-one applications AS Tuzhilin US Patent 6,236,978, 2001	1039	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	1011	1996
10 challenging problems in data mining research Q Yang, X Wu International Journal of Information Technology & Decision Making 5 (04 …, 2006	807	2006
System and method for dynamic profiling of users in one-to-one applications and for validating user rules AS Tuzhilin, G Adomavicius US Patent 7,603,331, 2009	626*	2009
On subjective measures of interestingness in knowledge discovery. A Silberschatz, A Tuzhilin KDD 95, 275-281, 1995	564	1995
Personalization technologies: a process-oriented perspective G Adomavicius, A Tuzhilin Communications of the ACM 48 (10), 83-90, 2005	487	2005
Using data mining methods to build customer profiles G Adomavicius, A Tuzhilin Computer 34 (2), 74-82, 2001	408	2001
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	379	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	378	2008
A Belief-Driven Method for Discovering Unexpected Patterns. B Padmanabhan, A Tuzhilin KDD 98, 94-100, 1998	372	1998
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	279	2008
Unexpectedness as a measure of interestingness in knowledge discovery B Padmanabhan, A Tuzhilin Decision Support Systems 27 (3), 303-318, 1999	279	1999
On unexpectedness in recommender systems: Or how to except the unexpected P Adamopoulos, A Tuzhilin RecSys Workshop on Novelty and Diversity in Recommender Systems, 2011	256*	2011
Expert-driven validation of rule-based user models in personalization applications G Adomavicius, A Tuzhilin Data Mining and Knowledge Discovery 5 (1), 33-58, 2001	256	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	250	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	204	1999
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	196	2000

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