Measuring retrieval effectiveness based on user preference of documents
Journal of the American Society for Information Science
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Text-Learning and Related Intelligent Agents: A Survey
IEEE Intelligent Systems
Adaptive, intelligent presentation of information for the museum visitor in PEACH
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
Interactive User Modeling for Personalized Access to Museum Collections: The Rijksmuseum Case Study
UM '07 Proceedings of the 11th international conference on User Modeling
Integrating tags in a semantic content-based recommender
Proceedings of the 2008 ACM conference on Recommender systems
Combining learning and word sense disambiguation for intelligent user profiling
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A folksonomy-based recommender system for personalized access to digital artworks
Journal on Computing and Cultural Heritage (JOCCH)
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Basic content-based personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, with the attributes of a content object. The Web 2.0 (r)evolution has changed the game for personalization, from `elitary' Web 1.0, written by few and read by many, to web content generated by everyone (user-generated content - UGC), since the role of people has evolved from passive consumers of information to that of active contributors. One of the forms of UGC that has drawn most attention of the research community is folksonomy, a taxonomy generated by users who collaboratively annotate and categorize resources of interests with freely chosen keywords called tags. FIRSt (F olksonomy-based I tem R ecommender sySt em) is a content-based recommender system developed at the University of Bari 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 to annotate rated items with free tags. FIRSt is capable of providing recommendations for items in several domains (e.g., movies, music, books), provided that descriptions of items are available as text documents (e.g. plot summaries, reviews, short abstracts). This paper describes the system general architecture and user modeling approach, showing how this recommendation model has been applied to recommend the artworks located at the Vatican Picture Gallery (Pinacoteca Vaticana), providing users with a personalized museum tour tailored on their tastes.