Automatic personalization based on Web usage mining
Communications of the ACM
Personal ontologies for web navigation
Proceedings of the ninth international conference on Information and knowledge management
Web montage: a dynamic personalized start page
Proceedings of the 11th international conference on World Wide Web
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
Learning What People (Don't) Want
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Web Usage Mining as a Tool for Personalization: A Survey
User Modeling and User-Adapted Interaction
Web usage mining based on probabilistic latent semantic analysis
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Navigation behavior models for link structure optimization
User Modeling and User-Adapted Interaction
MODELING WEB NAVIGATION USING GRAMMATICAL INFERENCE
Applied Artificial Intelligence
A survey of Web clustering engines
ACM Computing Surveys (CSUR)
Ontological technologies for user modelling
International Journal of Metadata, Semantics and Ontologies
Incorporating concept hierarchies into usage mining based recommendations
WebKDD'06 Proceedings of the 8th Knowledge discovery on the web international conference on Advances in web mining and web usage analysis
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This paper improves a recently-presented approach to Web Personalization, named Community Web Directories, which applies personalization techniques to Web Directories. The Web directory is viewed as a concept hierarchy and personalization is realized by constructing user community models on the basis of usage data collected by the proxy servers of an Internet Service Provider. The user communities are modeled using Probabilistic Latent Semantic Analysis (PLSA), which provides a number of advantages such as overlapping communities, as well as a good rationale for the associations that exist in the data. The data that are analyzed present challenging peculiarities such as their large volume and semantic diversity. Initial results presented in this paper illustrate the effectiveness of the new method.