GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
ANATAGONOMY: a personalized newspaper on the World Wide Web
International Journal of Human-Computer Studies - Special issue: innovative applications of the World Wide Web
Information filtering via hill climbing, WordNet and index patterns
Information Processing and Management: an International Journal - Special issue on electronic news
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Collecting user access patterns for building user profiles and collaborative filtering
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
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
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
Discovering User Interests from Web Browsing Behavior: An Application to Internet News Services
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 7 - Volume 7
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Newsjunkie: providing personalized newsfeeds via analysis of information novelty
Proceedings of the 13th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Comparing and evaluating information retrieval algorithms for news recommendation
Proceedings of the 2007 ACM conference on Recommender systems
News@hand: A Semantic Web Approach to Recommending News
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Collaborative filtering adapted to recommender systems of e-learning
Knowledge-Based Systems
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Personalized news recommendation based on click behavior
Proceedings of the 15th international conference on Intelligent user interfaces
Improved trust-aware recommender system using small-worldness of trust networks
Knowledge-Based Systems
Collaborative filtering recommender systems
The adaptive web
Content-based recommendation systems
The adaptive web
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
IEEE Transactions on Audio, Speech, and Language Processing
Using past-prediction accuracy in recommender systems
Information Sciences: an International Journal
Identifying the semantic orientation of terms using S-HAL for sentiment analysis
Knowledge-Based Systems
Towards a user based recommendation strategy for digital ecosystems
Knowledge-Based Systems
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News recommendation is a very active research field. The number of online journals has increased in recent years owing to the increasing popularity of the Internet. In this context, it is important to offer user tools that facilitate faster and more accurate access to articles of interest in digital newspapers. We present two probabilistic models based on latent variables that recommend relevant news to users according to profiles of their visits to the newspaper website. As input, the models consider news content and categories, according to a predefined classification, of those news previously accessed. The experimental results show good performance with respect to baseline models in a data set of news extracted from a digital journal edition.