Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Information Retrieval
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Learning What People (Don't) Want
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Clustering Approach for Hybrid Recommender System
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IEEE Transactions on Knowledge and Data Engineering
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
A hybrid approach for movie recommendation
Multimedia Tools and Applications
A collaborative recommender system based on probabilistic inference from fuzzy observations
Fuzzy Sets and Systems
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A unified approach to building hybrid recommender systems
Proceedings of the third ACM conference on Recommender systems
Collaborative filtering using interval estimation naïve Bayes
AWIC'03 Proceedings of the 1st international Atlantic web intelligence conference on Advances in web intelligence
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Structure and parameter learning for causal independence and causal interaction models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A decision-based approach for recommending in hierarchical domains
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A symbolic hybrid approach to face the new user problem in recommender systems
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
IEEE Transactions on Audio, Speech, and Language Processing
Semantically enhanced collaborative filtering based on RSVD
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
Hybrid recommendation based on low-dimensional augmentation of combined feature profiles
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
Personalized book recommendations created by using social media data
WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
Improving neighborhood based Collaborative Filtering via integrated folksonomy information
Pattern Recognition Letters
A latent model for collaborative filtering
International Journal of Approximate Reasoning
Predicting the ratings of multimedia items for making personalized recommendations
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Sparse linear methods with side information for top-n recommendations
Proceedings of the sixth ACM conference on Recommender systems
Content-based and collaborative techniques for tag recommendation: an empirical evaluation
Journal of Intelligent Information Systems
A hybrid recommendation approach for a tourism system
Expert Systems with Applications: An International Journal
Information Processing and Management: an International Journal
Cluster searching strategies for collaborative recommendation systems
Information Processing and Management: an International Journal
A method for collaborative recommendation in document retrieval systems
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Improving collaborative filtering-based recommender systems results using Pareto dominance
Information Sciences: an International Journal
Knowledge-Based Systems
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Trust based recommendation systems
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Leveraging biosignal and collaborative filtering for context-aware recommendation
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
Towards effective course-based recommendations for public tenders
International Journal of Knowledge and Web Intelligence
Intelligent patent recommendation system for innovative design collaboration
Journal of Network and Computer Applications
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Recommender systems enable users to access products or articles that they would otherwise not be aware of due to the wealth of information to be found on the Internet. The two traditional recommendation techniques are content-based and collaborative filtering. While both methods have their advantages, they also have certain disadvantages, some of which can be solved by combining both techniques to improve the quality of the recommendation. The resulting system is known as a hybrid recommender system. In the context of artificial intelligence, Bayesian networks have been widely and successfully applied to problems with a high level of uncertainty. The field of recommendation represents a very interesting testing ground to put these probabilistic tools into practice. This paper therefore presents a new Bayesian network model to deal with the problem of hybrid recommendation by combining content-based and collaborative features. It has been tailored to the problem in hand and is equipped with a flexible topology and efficient mechanisms to estimate the required probability distributions so that probabilistic inference may be performed. The effectiveness of the model is demonstrated using the MovieLens and IMDB data sets.