Fab: content-based, collaborative recommendation
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Clustering Algorithms
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Improving User Modelling with Content-Based Techniques
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
User Modeling and User-Adapted Interaction
Combining learning and word sense disambiguation for intelligent user profiling
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Word sense disambiguation for exploiting hierarchical thesauri in text classification
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Boosting for text classification with semantic features
WebKDD'04 Proceedings of the 6th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Learning semantic user profiles from text
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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
Application of Item Response Theory to Collaborative Filtering
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Content-Based Personalization Services Integrating Folksonomies
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
| Hi-index | 0.00 |
Collaborative filtering algorithms predict the preferences of a user for an item by weighting the contributions of similarusers, called neighbors, for that item. Similarity between users is computed by comparing their rating styles, i.e. the set of ratings given on the sameitems. Unfortunately, similarity between users is computable only if they have common rated items. The main contribution of this paper is a (content-collaborative) hybrid recommender system which overcomes this limitation by computing similarity between users on the ground of their content-based profiles. Traditional keyword-based profiles are unable to capture the semanticsof user interests, due to the natural language ambiguity. A distinctive feature of the proposed technique is that a statistical model of the user interests is obtained by machine learning techniques integrated with linguistic knowledge contained in the WordNet lexical database. This model, called the semanticuser profile, is exploited by the hybrid recommender in the neighborhood formation process. The results of an experimental session in a movie recommendation scenario demonstrate the effectiveness of the proposed approach.