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
Konstantin BaumanAssistant Professor, Fox School of Business, Temple UniversityVerified email at temple.edu
Panagiotis (Panos) AdamopoulosGoizueta Business School, Emory UniversityVerified email at stern.nyu.edu
A SilberschatzProfessor of Computer Science at Yale UniversityVerified email at yale.edu
Pan LiPhD, New York University, Stern School of BusinessVerified email at stern.nyu.edu
Hui Xiong, Fellow of AAAS and IEEERutgers University/Baidu/HKUSTVerified email at ust.hk
Yong GeAssociate Professor of MIS at University of ArizonaVerified email at email.arizona.edu
Bing LiuDistinguished Professor, University of Illinois at ChicagoVerified email at uic.edu
Bamshad MobasherSchool of Computing, DePaul UniversityVerified email at cs.depaul.edu
Akhmed UmyarovUniversity of Minnesota. Carlson School of Management.Verified email at umn.edu
Rong ZhengAssociate Professor of Information System, HKUSTVerified email at ust.hk
Michael J. PazzaniPrincipal ScientistVerified email at isi.edu
Dietmar JannachUniversity of Klagenfurt, Austria; University of Bergen, NorwayVerified email at aau.at
Markus ZankerFree University of Bolzano-Bozen, Italy; University of Klagenfurt, AustriaVerified 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
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	13942	2005
Context-aware recommender systems G Adomavicius, A Tuzhilin Recommender systems handbook, 217-253, 2010	3021	2010
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	1711	2005
System and method for dynamic profiling of users in one-to-one applications AS Tuzhilin US Patent 6,236,978, 2001	1157	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	1050	1996
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	746*	2009
On subjective measures of interestingness in knowledge discovery. A Silberschatz, A Tuzhilin KDD 95, 275-281, 1995	598	1995
Personalization technologies: a process-oriented perspective G Adomavicius, A Tuzhilin Communications of the ACM 48 (10), 83-90, 2005	549	2005
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	513	2008
Using data mining methods to build customer profiles G Adomavicius, A Tuzhilin Computer 34 (2), 74-82, 2001	428	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	419	2010
A Belief-Driven Method for Discovering Unexpected Patterns. B Padmanabhan, A Tuzhilin KDD 98, 94-100, 1998	382	1998
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	332*	2011
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	303	2008
Unexpectedness as a measure of interestingness in knowledge discovery B Padmanabhan, A Tuzhilin Decision Support Systems 27 (3), 303-318, 1999	288	1999
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	278	2009
Expert-driven validation of rule-based user models in personalization applications G Adomavicius, A Tuzhilin Data Mining and Knowledge Discovery 5, 33-58, 2001	258	2001
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	216	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	200	2000
Comparing context-aware recommender systems in terms of accuracy and diversity U Panniello, A Tuzhilin, M Gorgoglione User Modeling and User-Adapted Interaction 24, 35-65, 2014	189	2014

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