The vocabulary problem in human-system communication
Communications of the ACM
Using latent semantic analysis to improve access to textual information
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Automatic personalization based on Web usage mining
Communications of the ACM
SEWeP: using site semantics and a taxonomy to enhance the Web personalization process
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
User Modelling for News Web Sites with Word Sense Based Techniques
User Modeling and User-Adapted Interaction
Measuring semantic similarity in the taxonomy of WordNet
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Clickstream Log Acquisition with Web Farming
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
C-SAW---contextual semantic alignment of ontologies: using negative semantic reinforcement
Proceedings of the 2008 ACM symposium on Applied computing
Personalized recommendation of related content based on automatic metadata extraction
CASCON '08 Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds
ICADL 08 Proceedings of the 11th International Conference on Asian Digital Libraries: Universal and Ubiquitous Access to Information
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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Content-based implicit user modeling techniques usually employ a traditional term vector as a representation of the user's interest. However, due to the problem of dimensionality in the vector space model, a simple term vector is not a sufficient representation of the user model as it ignores the semantic relations between terms. In this paper, we present a novel method to enhance a traditional term-based user model with WordNet-based semantic similarity techniques. To achieve this, we use word definitions and relationship hierarchies in WordNet to perform word sense disambiguation and employ domain-specific concepts as category labels for the derived user models. We tested our method on Windows to the Universe, a public educational website covering subjects in the Earth and Space Sciences, and performed an evaluation of our semantically enhanced user models against human judgment. Our approach is distinguishable from existing work because we automatically narrow down the set of domain specific concepts from initial domain concepts obtained from Wikipedia and because we automatically create semantically enhanced user models.