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
Citations
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Proceedings ArticleDOI
Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval
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Combining naive bayes and n-gram language models for text classification
Fuchun Peng,Dale Schuurmans +1 more
TL;DR: This work augments the naive Bayes model with an n-gram language model to address two shortcomings of naive Baye text classifiers and shows that smoothing techniques from statistical language modeling can be used to recover better estimates than the Laplace smoothing technique usually used in naive Bayed classification.
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Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations
TL;DR: It is shown that the extraction of opinions from free-text reviews can improve the accuracy of movie recommendations and the opinion mining based features perform significantly better than the baseline, which is based on star ratings and genre information only.
Journal ArticleDOI
A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation
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Proceedings ArticleDOI
Learning implicit user interest hierarchy for context in personalization
Hyoung-Rae Kim,Philip K. Chan +1 more
TL;DR: This paper proposes a divisive hierarchical clustering algorithm to group words (topics) into a hierarchy where more general interests are represented by a larger set of words, and proposes a few similarity functions and dynamic threshold-finding methods that evaluate the resulting hierarchies according to their meaningfulness and shape.
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