| 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 | 10892 | 2005 |
| Context-aware recommender systems G Adomavicius, A Tuzhilin Recommender systems handbook, 217-253, 2011 | 2177 | 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 | 1397 | 2005 |
| System and method for dynamic profiling of users in one-to-one applications AS Tuzhilin US Patent 6,236,978, 2001 | 965 | 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 | 963 | 1996 |
| 10 challenging problems in data mining research Q Yang, X Wu International Journal of Information Technology & Decision Making 5 (04 …, 2006 | 704 | 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 | 565* | 2012 |
| On subjective measures of interestingness in knowledge discovery. A Silberschatz, A Tuzhilin KDD 95, 275-281, 1995 | 541 | 1995 |
| Personalization technologies: a process-oriented perspective G Adomavicius, A Tuzhilin Communications of the ACM 48 (10), 83-90, 2005 | 430 | 2005 |
| Using data mining methods to build customer profiles G Adomavicius, A Tuzhilin Computer 34 (2), 74-82, 2001 | 375 | 2001 |
| A Belief-Driven Method for Discovering Unexpected Patterns. B Padmanabhan, A Tuzhilin KDD 98, 94-100, 1998 | 364 | 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 | 312 | 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 | 306 | 2008 |
| Unexpectedness as a measure of interestingness in knowledge discovery B Padmanabhan, A Tuzhilin Decision Support Systems 27 (3), 303-318, 1999 | 263 | 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 | 238 | 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 | 218 | 2009 |
| 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 | 194 | 2015 |
| 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 | 194 | 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 | 188 | 2000 |
Gediminas AdomaviciusProfessor of Information and Decision Sciences, University of MinnesotaVerified email at umn.edu
