On the optimality of the simple Bayesian classifier under zero-one loss P Domingos, M Pazzani Machine learning 29 (2-3), 103-130, 1997 | 4630 | 1997 |
Content-based recommendation systems MJ Pazzani, D Billsus The adaptive web: methods and strategies of web personalization, 325-341, 2007 | 3945 | 2007 |
A framework for collaborative, content-based and demographic filtering MJ Pazzani Artificial intelligence review 13, 393-408, 1999 | 2444 | 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 | 2194 | 2001 |
Learning and revising user profiles: The identification of interesting web sites M Pazzani, D Billsus Machine learning 27, 313-331, 1997 | 2053 | 1997 |
Learning collaborative information filters. D Billsus, MJ Pazzani Icml 98, 46-54, 1998 | 1897 | 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 | 1678 | 2001 |
Syskill & Webert: Identifying interesting web sites MJ Pazzani, J Muramatsu, D Billsus AAAI/IAAI, Vol. 1, 54-61, 1996 | 1253 | 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 | 1245 | 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 | 1227 | 1996 |
Scaling up dynamic time warping for datamining applications EJ Keogh, MJ Pazzani Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000 | 1109 | 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 | 934 | 2004 |
User modeling for adaptive news access D Billsus, MJ Pazzani User modeling and user-adapted interaction 10, 147-180, 2000 | 914 | 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 | 844 | 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 | 679 | 1999 |
Machine learning for user modeling GI Webb, MJ Pazzani, D Billsus User modeling and user-adapted interaction 11, 19-29, 2001 | 635 | 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 | 569 | 2002 |
Detecting group differences: Mining contrast sets SD Bay, MJ Pazzani Data mining and knowledge discovery 5, 213-246, 2001 | 550 | 2001 |
Reducing misclassification costs M Pazzani, C Merz, P Murphy, K Ali, T Hume, C Brunk Machine Learning Proceedings 1994, 217-225, 1994 | 525 | 1994 |
The utility of knowledge in inductive learning M Pazzani, D Kibler Machine learning 9, 57-94, 1992 | 504 | 1992 |