The SCEAS System
Navigation Menu
Introduction
Collection Based
Clustering
Ranking
Paper Based
Journals
Conferences
DB Status
Statistics
Search

Search the dblp DataBase

Title:
Author:
Michael J. Pazzani: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Michael J. Pazzani, Daniel Billsus
    Learning and Revising User Profiles: The Identification of Interesting Web Sites. [Citation Graph (2, 0)][DBLP]
    Machine Learning, 1997, v:27, n:3, pp:313-331 [Journal]
  2. Michael J. Pazzani, Dennis F. Kibler
    The Utility of Knowledge in Inductive Learning. [Citation Graph (2, 0)][DBLP]
    Machine Learning, 1992, v:9, n:, pp:57-94 [Journal]
  3. Pedro Domingos, Michael J. Pazzani
    Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. [Citation Graph (1, 0)][DBLP]
    ICML, 1996, pp:105-112 [Conf]
  4. Michael J. Pazzani, Clifford Brunk, Glenn Silverstein
    A Knowledge-intensive Approach to Learning Relational Concepts. [Citation Graph (1, 0)][DBLP]
    ML, 1991, pp:432-436 [Conf]
  5. Michael J. Pazzani
    Detecting and Correcting Errors of Omission After Explanation-Based Learning. [Citation Graph (1, 0)][DBLP]
    IJCAI, 1989, pp:713-718 [Conf]
  6. Michael J. Pazzani, Michael G. Dyer, Margot Flowers
    Using Prior Learning to Facilitate the Learning of New Causal Theories. [Citation Graph (1, 0)][DBLP]
    IJCAI, 1987, pp:277-279 [Conf]
  7. Mark S. Ackerman, Daniel Billsus, Scott Gaffney, Seth Hettich, Gordon Khoo, Dong Joon Kim, Raymond Klefstad, Charles Lowe, Alexius Ludeman, Jack Muramatsu, Kazuo Omori, Michael J. Pazzani, Douglas Semler, Brian Starr, Paul Yap
    Learning Probabilistic User Profiles: Applications for Finding Interesting Web Sites, Notifying Users of Relevant Changes to Web Pages, and Locating Grant Opportunities. [Citation Graph (1, 0)][DBLP]
    AI Magazine, 1997, v:18, n:2, pp:47-56 [Journal]
  8. Pedro Domingos, Michael J. Pazzani
    Simple Bayesian Classifiers Do Not Assume Independence. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, Vol. 2, 1996, pp:1386- [Conf]
  9. Richard H. Lathrop, Nicholas R. Steffen, Miriam P. Raphael, Sophia Deeds-Rubin, Michael J. Pazzani, Paul J. Cimoch, Darryl M. See, Jeremiah G. Tilles
    Knowledge-Based Avoidance of Drug-Resistant HIV Mutants. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 1998, pp:1071-1078 [Conf]
  10. Michael J. Pazzani
    Interactive Script Instantiation. [Citation Graph (0, 0)][DBLP]
    AAAI, 1983, pp:320-326 [Conf]
  11. Michael J. Pazzani
    Refining the Knowledge Base of a Diagnostic Expert System: An Application of Failure-Driven Learning. [Citation Graph (0, 0)][DBLP]
    AAAI, 1986, pp:1029-1035 [Conf]
  12. Michael J. Pazzani, Clifford Brunk
    Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning. [Citation Graph (0, 0)][DBLP]
    AAAI, 1993, pp:328-334 [Conf]
  13. Michael J. Pazzani, Michael G. Dyer, Margot Flowers
    The Role of Prior Causal Theories in Generalization. [Citation Graph (0, 0)][DBLP]
    AAAI, 1986, pp:545-550 [Conf]
  14. Michael J. Pazzani, Jack Muramatsu, Daniel Billsus
    Syskill & Webert: Identifying Interesting Web Sites. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, Vol. 1, 1996, pp:54-61 [Conf]
  15. Daniel Billsus, Michael J. Pazzani
    A Personal News Agent That Talks, Learns and Explains. [Citation Graph (0, 0)][DBLP]
    Agents, 1999, pp:268-275 [Conf]
  16. Michael J. Pazzani, Daniel Billsus
    Adaptive Web Site Agents. [Citation Graph (0, 0)][DBLP]
    Agents, 1999, pp:394-395 [Conf]
  17. Subramani Mani, Malcolm B. Dick, Michael J. Pazzani, Evelyn L. Teng, Daniel Kempler, I. Maribell Taussig
    Refinement of Neuro-psychological Tests for Dementia Screening in a Cross Cultural Population Using Machine Learning. [Citation Graph (0, 0)][DBLP]
    AIMDM, 1999, pp:326-335 [Conf]
  18. Subramani Mani, Michael J. Pazzani, John West
    Knowledge Discovery from a Breast Cancer Database. [Citation Graph (0, 0)][DBLP]
    AIME, 1997, pp:130-133 [Conf]
  19. William Rodman Shankle, Subramani Mani, Michael J. Pazzani, Padhraic Smyth
    Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods. [Citation Graph (0, 0)][DBLP]
    AIME, 1997, pp:73-85 [Conf]
  20. Michael J. Pazzani, Carl Engelman
    Knowledge Based Question Answering. [Citation Graph (0, 0)][DBLP]
    ANLP, 1983, pp:73-80 [Conf]
  21. Geoffrey I. Webb, Michael J. Pazzani
    Adjusted Probability Naive Bayesian Induction. [Citation Graph (0, 0)][DBLP]
    Australian Joint Conference on Artificial Intelligence, 1998, pp:285-295 [Conf]
  22. Michael J. Pazzani
    Conceptual Analysis of Garden-Path Sentences. [Citation Graph (0, 0)][DBLP]
    COLING, 1984, pp:486-490 [Conf]
  23. Michael J. Pazzani
    Learning with Globally Predictive Tests. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 1998, pp:220-231 [Conf]
  24. Michael J. Pazzani
    Integrating Explanation-Based and Empirical Learning Methods in OCCAM. [Citation Graph (0, 0)][DBLP]
    EWSL, 1988, pp:147-165 [Conf]
  25. Eamonn J. Keogh, Selina Chu, David Hart, Michael J. Pazzani
    An Online Algorithm for Segmenting Time Series. [Citation Graph (0, 0)][DBLP]
    ICDM, 2001, pp:289-296 [Conf]
  26. Stephen D. Bay, Michael J. Pazzani
    Characterizing Model Erros and Differences. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:49-56 [Conf]
  27. Daniel Billsus, Michael J. Pazzani
    Learning Collaborative Information Filters. [Citation Graph (0, 0)][DBLP]
    ICML, 1998, pp:46-54 [Conf]
  28. Clifford Brunk, Michael J. Pazzani
    An Investigation of Noise-Tolerant Relational Concept Learning Algorithms. [Citation Graph (0, 0)][DBLP]
    ML, 1991, pp:389-393 [Conf]
  29. Clifford Brunk, Michael J. Pazzani
    A Lexical Based Semantic Bias for Theory Revision. [Citation Graph (0, 0)][DBLP]
    ICML, 1995, pp:81-89 [Conf]
  30. Daniel S. Hirschberg, Michael J. Pazzani
    Average Case Analysis of Learning kappa-CNF Concepts. [Citation Graph (0, 0)][DBLP]
    ML, 1992, pp:206-211 [Conf]
  31. Patrick M. Murphy, Michael J. Pazzani
    Constructive Induction of M-of-N Terms. [Citation Graph (0, 0)][DBLP]
    ML, 1991, pp:183-187 [Conf]
  32. Patrick M. Murphy, Michael J. Pazzani
    Revision of Production System Rule-Bases. [Citation Graph (0, 0)][DBLP]
    ICML, 1994, pp:199-207 [Conf]
  33. Michael J. Pazzani
    Integrated Learning with Incorrect and Incomplete Theories. [Citation Graph (0, 0)][DBLP]
    ML, 1988, pp:291-297 [Conf]
  34. Michael J. Pazzani
    Explanation-Based Learning with Week Domain Theories. [Citation Graph (0, 0)][DBLP]
    ML, 1989, pp:72-74 [Conf]
  35. Michael J. Pazzani, Christopher J. Merz, Patrick M. Murphy, Kamal Ali, Timothy Hume, Clifford Brunk
    Reducing Misclassification Costs. [Citation Graph (0, 0)][DBLP]
    ICML, 1994, pp:217-225 [Conf]
  36. Michael J. Pazzani, Wendy Sarrett
    Average Case Analysis of Conjunctive Learning Algorithms. [Citation Graph (0, 0)][DBLP]
    ML, 1990, pp:339-347 [Conf]
  37. Wendy Sarrett, Michael J. Pazzani
    One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning. [Citation Graph (0, 0)][DBLP]
    ML, 1989, pp:26-28 [Conf]
  38. Glenn Silverstein, Michael J. Pazzani
    Relational Clichés: Constraining Induction During Relational Learning. [Citation Graph (0, 0)][DBLP]
    ML, 1991, pp:203-207 [Conf]
  39. Takefumi Yamazaki, Michael J. Pazzani, Christopher J. Merz
    Learning Hierarchies from Ambiguous Natural Language Data. [Citation Graph (0, 0)][DBLP]
    ICML, 1995, pp:575-583 [Conf]
  40. Kamal Ali, Clifford Brunk, Michael J. Pazzani
    On Learning Multiple Descriptions of a Concept. [Citation Graph (0, 0)][DBLP]
    ICTAI, 1994, pp:476-483 [Conf]
  41. Christopher J. Merz, Michael J. Pazzani
    Parameter Tuning for the MAX Expert System. [Citation Graph (0, 0)][DBLP]
    ICTAI, 1994, pp:632-639 [Conf]
  42. Michael J. Pazzani
    Machine Learning for Personalized Wireless Portals. [Citation Graph (0, 0)][DBLP]
    ICTAI, 2004, pp:3- [Conf]
  43. Kamal M. Ali, Michael J. Pazzani
    HYDRA: A Noise-tolerant Relational Concept Learning Algorithm. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1993, pp:1064-1071 [Conf]
  44. Michael J. Pazzani, Michael G. Dyer
    A Comparison of Concept Identification in Human Learning and Network Learning with the Generalized Delta Rule. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1987, pp:147-150 [Conf]
  45. James Wogulis, Michael J. Pazzani
    A Methodology for Evaluating Theory Revision Systems: Results with Audrey II. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1993, pp:1128-1134 [Conf]
  46. Takefumi Yamazaki, Michael J. Pazzani, Christopher J. Merz
    Acquiring and updating hierarchical knowledge for machine translation based on a clustering technique. [Citation Graph (0, 0)][DBLP]
    Learning for Natural Language Processing, 1995, pp:329-342 [Conf]
  47. Daniel Billsus, Michael J. Pazzani, James Chen
    A learning agent for wireless news access. [Citation Graph (0, 0)][DBLP]
    Intelligent User Interfaces, 2000, pp:33-36 [Conf]
  48. Michael J. Pazzani
    Representation of electronic mail filtering profiles: a user study. [Citation Graph (0, 0)][DBLP]
    Intelligent User Interfaces, 2000, pp:202-206 [Conf]
  49. Stephen D. Bay, Michael J. Pazzani
    Detecting Change in Categorical Data: Mining Contrast Sets. [Citation Graph (0, 0)][DBLP]
    KDD, 1999, pp:302-306 [Conf]
  50. Seth Hettich, Michael J. Pazzani
    Mining for proposal reviewers: lessons learned at the national science foundation. [Citation Graph (0, 0)][DBLP]
    KDD, 2006, pp:862-871 [Conf]
  51. Eamonn J. Keogh, Selina Chu, Michael J. Pazzani
    Ensemble-index: a new approach to indexing large databases. [Citation Graph (0, 0)][DBLP]
    KDD, 2001, pp:117-125 [Conf]
  52. Eamonn J. Keogh, Michael J. Pazzani
    Scaling up dynamic time warping for datamining applications. [Citation Graph (0, 0)][DBLP]
    KDD, 2000, pp:285-289 [Conf]
  53. Eamonn J. Keogh, Michael J. Pazzani
    An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. [Citation Graph (0, 0)][DBLP]
    KDD, 1998, pp:239-243 [Conf]
  54. Michael J. Pazzani
    An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers. [Citation Graph (0, 0)][DBLP]
    KDD, 1995, pp:228-233 [Conf]
  55. Michael J. Pazzani, Subramani Mani, William Rodman Shankle
    Beyond Concise and Colorful: Learning Intelligible Rules. [Citation Graph (0, 0)][DBLP]
    KDD, 1997, pp:235-238 [Conf]
  56. Michael J. Pazzani
    Creating High Level Knowledge Structures from Simple Elements. [Citation Graph (0, 0)][DBLP]
    Knowledge Representation and Organization in Machine Learning, 1987, pp:258-288 [Conf]
  57. Giovanni Semeraro, Floriana Esposito, Donato Malerba, Clifford Brunk, Michael J. Pazzani
    Avoiding Non-Termination when Learning Logical Programs: A Case Study with FOIL and FOCL. [Citation Graph (0, 0)][DBLP]
    LOPSTR, 1994, pp:183-198 [Conf]
  58. Christopher J. Merz, Michael J. Pazzani
    Combining Neural Network Regression Estimates with Regularized Linear Weights. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:564-570 [Conf]
  59. Eamonn J. Keogh, Michael J. Pazzani
    A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2000, pp:122-133 [Conf]
  60. Eamonn J. Keogh, Michael J. Pazzani
    Scaling up Dynamic Time Warping to Massive Dataset. [Citation Graph (0, 0)][DBLP]
    PKDD, 1999, pp:1-11 [Conf]
  61. Koji Miyahara, Michael J. Pazzani
    Collaborative Filtering with the Simple Bayesian Classifier. [Citation Graph (0, 0)][DBLP]
    PRICAI, 2000, pp:679-689 [Conf]
  62. Michael J. Pazzani
    Commercial Applications of Machine Learning for Personalized Wireless Portals. [Citation Graph (0, 0)][DBLP]
    PRICAI, 2002, pp:1-5 [Conf]
  63. Selina Chu, Eamonn J. Keogh, David Hart, Michael J. Pazzani
    Iterative Deepening Dynamic Time Warping for Time Series. [Citation Graph (0, 0)][DBLP]
    SDM, 2002, pp:- [Conf]
  64. Eamonn J. Keogh, Michael J. Pazzani
    Relevance Feedback Retrieval of Time Series Data. [Citation Graph (0, 0)][DBLP]
    SIGIR, 1999, pp:183-190 [Conf]
  65. Eamonn J. Keogh, Kaushik Chakrabarti, Sharad Mehrotra, Michael J. Pazzani
    Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. [Citation Graph (0, 0)][DBLP]
    SIGMOD Conference, 2001, pp:151-162 [Conf]
  66. Eamonn J. Keogh, Michael J. Pazzani
    An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. [Citation Graph (0, 0)][DBLP]
    SSDBM, 1999, pp:56-67 [Conf]
  67. Michael J. Pazzani
    Adaptive Interfaces for Ubiquitous Web Access. [Citation Graph (0, 0)][DBLP]
    User Modeling, 2003, pp:1- [Conf]
  68. George Buchanan, Sarah Farrant, Matt Jones, Harold W. Thimbleby, Gary Marsden, Michael J. Pazzani
    Improving mobile internet usability. [Citation Graph (0, 0)][DBLP]
    WWW, 2001, pp:673-680 [Conf]
  69. Michael J. Pazzani, Daniel Billsus
    Adaptive Web Site Agents. [Citation Graph (0, 0)][DBLP]
    Autonomous Agents and Multi-Agent Systems, 2002, v:5, n:2, pp:205-218 [Journal]
  70. Richard H. Lathrop, Nicholas R. Steffen, Miriam P. Raphael, Sophia Deeds-Rubin, Michael J. Pazzani, Paul J. Cimoch, Darryl M. See, Jeremiah G. Tilles
    Knowledge-Based Avoidance of Drug-Resistant HIV Mutants. [Citation Graph (0, 0)][DBLP]
    AI Magazine, 1999, v:20, n:1, pp:13-25 [Journal]
  71. Michael J. Pazzani
    A Framework for Collaborative, Content-Based and Demographic Filtering. [Citation Graph (0, 0)][DBLP]
    Artif. Intell. Rev., 1999, v:13, n:5-6, pp:393-408 [Journal]
  72. Subramani Mani, William Rodman Shankle, Malcolm B. Dick, Michael J. Pazzani
    Two-Stage Machine Learning model for guideline development. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 1999, v:16, n:1, pp:51-71 [Journal]
  73. Serge Abiteboul, Rakesh Agrawal, Philip A. Bernstein, Michael J. Carey, Stefano Ceri, W. Bruce Croft, David J. DeWitt, Michael J. Franklin, Hector Garcia-Molina, Dieter Gawlick, Jim Gray, Laura M. Haas, Alon Y. Halevy, Joseph M. Hellerstein, Yannis E. Ioannidis, Martin L. Kersten, Michael J. Pazzani, Michael Lesk, David Maier, Jeffrey F. Naughton, Hans-Jörg Schek, Timos K. Sellis, Avi Silberschatz, Michael Stonebraker, Richard T. Snodgrass, Jeffrey D. Ullman, Gerhard Weikum, Jennifer Widom, Stanley B. Zdonik
    The Lowell database research self-assessment. [Citation Graph (0, 0)][DBLP]
    Commun. ACM, 2005, v:48, n:5, pp:111-118 [Journal]
  74. Daniel Billsus, Clifford Brunk, Craig Evans, Brian Gladish, Michael J. Pazzani
    Adaptive interfaces for ubiquitous web access. [Citation Graph (0, 0)][DBLP]
    Commun. ACM, 2002, v:45, n:5, pp:34-38 [Journal]
  75. Michael J. Pazzani
    A Computational Theory of Learning Causal Relationships. [Citation Graph (0, 0)][DBLP]
    Cognitive Science, 1991, v:15, n:3, pp:401-424 [Journal]
  76. Serge Abiteboul, Rakesh Agrawal, Philip A. Bernstein, Michael J. Carey, Stefano Ceri, W. Bruce Croft, David J. DeWitt, Michael J. Franklin, Hector Garcia-Molina, Dieter Gawlick, Jim Gray, Laura M. Haas, Alon Y. Halevy, Joseph M. Hellerstein, Yannis E. Ioannidis, Martin L. Kersten, Michael J. Pazzani, Michael Lesk, David Maier, Jeffrey F. Naughton, Hans-Jörg Schek, Timos K. Sellis, Avi Silberschatz, Michael Stonebraker, Richard T. Snodgrass, Jeffrey D. Ullman, Gerhard Weikum, Jennifer Widom, Stanley B. Zdonik
    The Lowell Database Research Self Assessment [Citation Graph (0, 0)][DBLP]
    CoRR, 2003, v:0, n:, pp:- [Journal]
  77. Stephen D. Bay, Michael J. Pazzani
    Detecting Group Differences: Mining Contrast Sets. [Citation Graph (0, 0)][DBLP]
    Data Min. Knowl. Discov., 2001, v:5, n:3, pp:213-246 [Journal]
  78. Christopher J. Merz, Michael J. Pazzani, Andrea Pohoreckyj Danyluk
    Tuning Numeric Parameters to Troubleshoot a Telephone-Network Loop. [Citation Graph (0, 0)][DBLP]
    IEEE Expert, 1996, v:11, n:1, pp:44-49 [Journal]
  79. Michael J. Pazzani
    Knowledge discovery from data? [Citation Graph (0, 0)][DBLP]
    IEEE Intelligent Systems, 2000, v:15, n:2, pp:10-13 [Journal]
  80. Eamonn J. Keogh, Michael J. Pazzani
    Learning the Structure of Augmented Bayesian Classifiers. [Citation Graph (0, 0)][DBLP]
    International Journal on Artificial Intelligence Tools, 2002, v:11, n:4, pp:587-601 [Journal]
  81. Michael J. Pazzani
    Explanation-Based Learning for Knowledge-Based Systems. [Citation Graph (0, 0)][DBLP]
    International Journal of Man-Machine Studies, 1987, v:26, n:4, pp:413-433 [Journal]
  82. Patrick M. Murphy, Michael J. Pazzani
    Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 1994, v:1, n:, pp:257-275 [Journal]
  83. Richard H. Lathrop, Michael J. Pazzani
    Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses. [Citation Graph (0, 0)][DBLP]
    J. Comb. Optim., 1999, v:3, n:2-3, pp:301-320 [Journal]
  84. Eamonn J. Keogh, Kaushik Chakrabarti, Michael J. Pazzani, Sharad Mehrotra
    Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. [Citation Graph (0, 0)][DBLP]
    Knowl. Inf. Syst., 2001, v:3, n:3, pp:263-286 [Journal]
  85. Kamal M. Ali, Michael J. Pazzani
    Error Reduction through Learning Multiple Descriptions. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1996, v:24, n:3, pp:173-202 [Journal]
  86. Pedro Domingos, Michael J. Pazzani
    On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1997, v:29, n:2-3, pp:103-130 [Journal]
  87. Michael J. Pazzani
    A Reply to Cohen's Book Review of Creating a Memory of Causal Relationships. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1993, v:10, n:, pp:185-190 [Journal]
  88. Michael J. Pazzani
    Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1993, v:11, n:, pp:173-194 [Journal]
  89. Michael J. Pazzani
    Guest Editor's Introduction. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1994, v:16, n:1-2, pp:7-9 [Journal]
  90. Michael J. Pazzani
    Review of ``Inductive Logic Programming: Techniques and Applications'' by Nada Lavrac, Saso Dzeroski. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1996, v:23, n:1, pp:103-108 [Journal]
  91. Christopher J. Merz, Michael J. Pazzani
    A Principal Components Approach to Combining Regression Estimates. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1999, v:36, n:1-2, pp:9-32 [Journal]
  92. Michael J. Pazzani, Wendy Sarrett
    A Framework for Average Case Analysis of Conjunctive Learning Algorithms. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1992, v:9, n:, pp:349-372 [Journal]
  93. Michael J. Pazzani
    Learning with Globally Predictive Tests. [Citation Graph (0, 0)][DBLP]
    New Generation Comput., 2000, v:18, n:1, pp:28-38 [Journal]
  94. Ian Soboroff, Charles K. Nicholas, Michael J. Pazzani
    Workshop on Recommender Systems: Algorithms and Evaluation. [Citation Graph (0, 0)][DBLP]
    SIGIR Forum, 1999, v:33, n:1, pp:36-43 [Journal]
  95. Stephen D. Bay, Dennis F. Kibler, Michael J. Pazzani, Padhraic Smyth
    The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2000, v:2, n:2, pp:81-85 [Journal]
  96. Kaushik Chakrabarti, Eamonn J. Keogh, Sharad Mehrotra, Michael J. Pazzani
    Locally adaptive dimensionality reduction for indexing large time series databases. [Citation Graph (0, 0)][DBLP]
    ACM Trans. Database Syst., 2002, v:27, n:2, pp:188-228 [Journal]
  97. Daniel Billsus, Michael J. Pazzani
    User Modeling for Adaptive News Access. [Citation Graph (0, 0)][DBLP]
    User Model. User-Adapt. Interact., 2000, v:10, n:2-3, pp:147-180 [Journal]
  98. Geoffrey I. Webb, Michael J. Pazzani, Daniel Billsus
    Machine Learning for User Modeling. [Citation Graph (0, 0)][DBLP]
    User Model. User-Adapt. Interact., 2001, v:11, n:1-2, pp:19-29 [Journal]
  99. Daniel Billsus, Michael J. Pazzani
    Adaptive News Access. [Citation Graph (0, 0)][DBLP]
    The Adaptive Web, 2007, pp:550-570 [Conf]
  100. Michael J. Pazzani, Daniel Billsus
    Content-Based Recommendation Systems. [Citation Graph (0, 0)][DBLP]
    The Adaptive Web, 2007, pp:325-341 [Conf]

  101. An energy-efficient mobile recommender system. [Citation Graph (, )][DBLP]


  102. The Do-I-Care Agent: Effective Social Discovery and Filtering on the Web. [Citation Graph (, )][DBLP]


Search in 0.037secs, Finished in 0.042secs
NOTICE1
System may not be available sometimes or not working properly, since it is still in development with continuous upgrades
NOTICE2
The rankings that are presented on this page should NOT be considered as formal since the citation info is incomplete in DBLP
 
Introduction Collection Based Clustering Ranking Paper Based Journals Conferences DB Status Statistics Search
System created by asidirop@csd.auth.gr [http://users.auth.gr/~asidirop/] © 2002
for Data Engineering Laboratory, Department of Informatics, Aristotle University © 2002