Measuring retrieval effectiveness based on user preference of documents
Journal of the American Society for Information Science
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
Learning Interaction Models in a Digital Library Service
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
IEEE Transactions on Knowledge and Data Engineering
Usage patterns of collaborative tagging systems
Journal of Information Science
Some Effective Techniques for Naive Bayes Text Classification
IEEE Transactions on Knowledge and Data Engineering
Adaptive, intelligent presentation of information for the museum visitor in PEACH
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
Improved recommendation based on collaborative tagging behaviors
Proceedings of the 13th international conference on Intelligent user interfaces
Interactive User Modeling for Personalized Access to Museum Collections: The Rijksmuseum Case Study
UM '07 Proceedings of the 11th international conference on User Modeling
Towards a Tag-Based User Model: How Can User Model Benefit from Tags?
UM '07 Proceedings of the 11th international conference on User Modeling
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Introducing Serendipity in a Content-Based Recommender System
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Integrating tags in a semantic content-based recommender
Proceedings of the 2008 ACM conference on Recommender systems
User Modeling and User-Adapted Interaction
Recommendations based on semantically enriched museum collections
Web Semantics: Science, Services and Agents on the World Wide Web
Hybrid Content and Tag-based Profiles for Recommendation in Collaborative Tagging Systems
LA-WEB '08 Proceedings of the 2008 Latin American Web Conference
Knowledge-Based Linguistic Annotation of Digital Cultural Heritage Collections
IEEE Intelligent Systems
Content-Based Personalization Services Integrating Folksonomies
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
UNIBA: JIGSAW algorithm for word sense disambiguation
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Combining learning and word sense disambiguation for intelligent user profiling
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Recommendations toward Serendipitous Diversions
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Using Keyword-Based Approaches to Adaptively Predict Interest in Museum Exhibits
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Content-based recommendation systems
The adaptive web
MARS: a MultilAnguage Recommender System
Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Using ontological and document similarity to estimate museum exhibit relatedness
Journal on Computing and Cultural Heritage (JOCCH)
A visitor's guide in an active museum: Presentations, communications, and reflection
Journal on Computing and Cultural Heritage (JOCCH)
Recommender Systems Handbook
Categorising social tags to improve folksonomy-based recommendations
Web Semantics: Science, Services and Agents on the World Wide Web
Cross-language information filtering: word sense disambiguation vs. distributional models
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Ontological access to images of fine art
Journal on Computing and Cultural Heritage (JOCCH)
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Museums have recognized the need for supporting visitors in fulfilling a personalized experience when visiting artwork collections, and they have started to adopt recommender systems as a way to meet this requirement. Content-based recommender systems analyze features of artworks previously rated by a visitor and build a visitor model or profile, in which preferences and interests are stored, based on those features. For example, the profile of a visitor might store the names of his or her favorite painters or painting techniques, extracted from short textual descriptions associated with artworks. The user profile is then matched against the attributes of new items in order to provide personalized suggestions. The Web 2.0 (r)evolution has changed the game for personalization from “elitist” Web 1.0, written by few and read by many, to Web content potentially generated by everyone (user-generated content - UGC). One of the forms of UGC that has drawn most attention from the research community is folksonomy, a taxonomy generated by users who collaboratively annotate and categorize resources of interests with freely chosen keywords called tags. In this work, we investigate the problem of deciding whether folksonomies might be a valuable source of information about user interests in the context of recommending digital artworks. We present FIRSt (Folksonomy-based Item Recommender syStem), a content-based recommender system which integrates UGC through social tagging in a classic content-based model, letting users express their preferences for items by entering a numerical rating as well as by annotating items with free tags. Experiments show that the accuracy of recommendations increases when tags are exploited in the recommendation process to enrich user profiles, provided that tags are not used as a surrogate for the item descriptions, but in conjunction with them. FIRSt has been developed within the CHAT project “Cultural Heritage fruition & e-learning applications of new Advanced (multimodal) Technologies”, and it is the core of a bouquet of Web services designed for personalized museum tours.