Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Reads0
Chats0
TLDR
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
More filters
Proceedings ArticleDOI
A Short-term User Interest Model for personalized recommendation
Jie Yu,Fangfang Liu +1 more
TL;DR: Short-term User Interest Model (SUIM) is presented to represent user's real-time interests based on his/her recent browsing content and behavior and memory capacity and recall probability from psychology are introduced to ensure the small scale and accuracy of SUIM.
Proceedings ArticleDOI
A user profile definition in context of recommendation of open educational resources. An approach based on linked open vocabularies
TL;DR: An open linked vocabulary is proposed to describe user profiles of the open educational resources, which take into account the challenges and opportunities that an open and extensible platform as the Web can provide to learn about the OER users, and from this knowledge, offer the most appropriate resources.
Proceedings ArticleDOI
An interactive agent system for supporting knowledge-based recommendation: a case study on an e-novel recommender system
TL;DR: This study designs an interactive agent system to support the knowledge-based recommendation and implements it in an e-novel web site, called Angel City, for recommending e- novels to readers.
Posted Content
Combining configuration and recommendation to define an interactive product line configuration approach
TL;DR: An approach that combines two complementary forms of guidance: configuration and recommendation, to help customers define their own products out of a product line specification is proposed, called interactive configuration.
Proceedings ArticleDOI
Inferential language modeling for selective Web search personalization and contextualization
TL;DR: The results of the initial experiment show that the proposed selective personalization and contextualization method underpinned by inferential language modeling significantly outperforms a baseline method developed based on syntactic click entropy.
References
More filters
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Journal ArticleDOI
Pattern Classification and Scene Analysis.
Book
Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
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.