M
Michael J. Pazzani
Researcher at University of California, Riverside
Publications - 190
Citations - 29519
Michael J. Pazzani is an academic researcher from University of California, Riverside. The author has contributed to research in topics: Explanation-based learning & Stability (learning theory). The author has an hindex of 62, co-authored 183 publications receiving 28036 citations. Previous affiliations of Michael J. Pazzani include University of California & Rutgers University.
Papers
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Journal ArticleDOI
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
TL;DR: The Bayesian classifier is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption, and will often outperform more powerful classifiers for common training set sizes and numbers of attributes, even if its bias is a priori much less appropriate to the domain.
Book ChapterDOI
Content-based recommendation systems
TL;DR: This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests, which are used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale.
Journal ArticleDOI
Dimensionality reduction for fast similarity search in large time series databases
TL;DR: This work introduces a new dimensionality reduction technique which it is called Piecewise Aggregate Approximation (PAA), and theoretically and empirically compare it to the other techniques and demonstrate its superiority.
Journal ArticleDOI
A Framework for Collaborative, Content-Based and Demographic Filtering
TL;DR: The types of information available to determine whether to recommend a particular page to a particular user are described and how each type of information may be used individually and an approach to combining recommendations from multiple sources are discussed.
Journal ArticleDOI
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
TL;DR: The use of a naive Bayesian classifier is described, and it is demonstrated that it can incrementally learn profiles from user feedback on the interestingness of Web sites and may easily be extended to revise user provided profiles.