Measuring retrieval effectiveness based on user preference of documents
Journal of the American Society for Information Science
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
OntoSeek: Content-Based Access to the Web
IEEE Intelligent Systems
An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
Improving Social Filtering Techniques Through WordNet-Based User Profiles
UM '07 Proceedings of the 11th international conference on User Modeling
The JIGSAW Algorithm for Word Sense Disambiguation and Semantic Indexing of Documents
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Discovering User Profiles from Semantically Indexed Scientific Papers
From Web to Social Web: Discovering and Deploying User and Content Profiles
Integrating tags in a semantic content-based recommender
Proceedings of the 2008 ACM conference on Recommender systems
Content-Based Personalization Services Integrating Folksonomies
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
SpIteR: A Module for Recommending Dynamic Personalized Museum Tours
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Ontological technologies for user modelling
International Journal of Metadata, Semantics and Ontologies
Semantic bayesian profiling services for information recommendation
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Content-based recommendation services for personalized digital libraries
DELOS'07 Proceedings of the 1st international conference on Digital libraries: research and development
MARS: a MultilAnguage Recommender System
Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Why finding entities in Wikipedia is difficult, sometimes
Information Retrieval
Leveraging the linkedin social network data for extracting content-based user profiles
Proceedings of the fifth ACM conference on Recommender systems
A folksonomy-based recommender system for personalized access to digital artworks
Journal on Computing and Cultural Heritage (JOCCH)
Information Sciences: an International Journal
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Understanding user interests from text documents can provide support to personalized information recommendation services. Typically, these services automatically infer the user profile, a structured model of the user interests, from documents that were already deemed relevant by the user. Traditional keyword-based approaches are unable to capture the semantics of the user interests. This work proposes the integration of linguistic knowledge in the process of learning semantic user profiles that capture concepts concerning user interests. The proposed strategy consists of two steps. The first one is based on a word sense disambiguation technique that exploits the lexical database WordNet to select, among all the possible meanings (senses) of a polysemous word, the correct one. In the second step, a naïve Bayes approach learns semantic sense-based user profiles as binary text classifiers (user-likes and user-dislikes) from disambiguated documents. Experiments have been conducted to compare the performance obtained by keyword-based profiles to that obtained by sense-based profiles. Both the classification accuracy and the effectiveness of the ranking imposed by the two different kinds of profile on the documents to be recommended have been considered. The main outcome is that the classification accuracy is increased with no improvement on the ranking. The conclusion is that the integration of linguistic knowledge in the learning process improves the classification of those documents whose classification score is close to the likes/dislikes threshold (the items for which the classification is highly uncertain).