Learning and Revising User Profiles: The Identification ofInteresting Web Sites
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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.Abstract:
We discuss algorithms for learning and revising user profiles that can determine which World Wide Web sites on a given topic would be interesting to a user. We describe the use of a naive Bayesian classifier for this task, and demonstrate that it can incrementally learn profiles from user feedback on the interestingness of Web sites. Furthermore, the Bayesian classifier may easily be extended to revise user provided profiles. In an experimental evaluation we compare the Bayesian classifier to computationally more intensive alternatives, and show that it performs at least as well as these approaches throughout a range of different domains. In addition, we empirically analyze the effects of providing the classifier with background knowledge in form of user defined profiles and examine the use of lexical knowledge for feature selection. We find that both approaches can substantially increase the prediction accuracy.read more
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Dissertation
Funktionen zur Orientierung in einem virtuellen, kollaborativen Wörterbuch (ENFORUM) - theoretische Grundlagen und Implementierung
TL;DR: In dieser Arbeit werden nach der grundlegenden Erörterung kognitiver Aspekte and der Ableitung of Erkenntnissen aus der Orientierung and Navigation in der Realwelt, mögliche Ausprägungen von Orientieringssmitteln in einer Taxonomie gegenübergestellt and eingeordnet.
Book ChapterDOI
The Role of Semantic Relevance in Dynamic User Community Management and the Formulation of Recommendations
TL;DR: A recommendation algorithm that is based on the maintenance of user profiles and their dynamic adjustment according to the users" behavior and which relies on the dynamic management of communities, which contain "similar" and "relevant" users and which are created according to a classification algorithm.
Learning implicit user interest hierarchy for web personalization
Philip K. Chan,Hyoung-rae Kim +1 more
TL;DR: Experimental results indicate that the personalized ranking methods presented in this study, when used with a popular search engine, can yield more relevant web pages for individual users, and the weighted term scoring function could provide more accurate ranking than Google on average.
Book
Web Page Recommendation Models: Theory and Algorithms
TL;DR: This monograph gives an overview of the research in the area of discovering and modeling the users' interest in order to recommend related Web pages and the Web page recommender systems studied in this monograph are categorized according to the data mining algorithms they use for recommendation.
References
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Book ChapterDOI
Learning internal representations by error propagation
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Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.