GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
Link prediction and path analysis using Markov chains
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Multidimensional binary search trees used for associative searching
Communications of the ACM
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
The Journal of Machine Learning Research
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
An MDP-Based Recommender System
The Journal of Machine Learning Research
Mining Complex Time-Series Data by Learning Markovian Models
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Open user profiles for adaptive news systems: help or harm?
Proceedings of the 16th international conference on World Wide Web
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Modeling Online Browsing and Path Analysis Using Clickstream Data
Marketing Science
Mining longest repeating subsequences to predict world wide web surfing
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
Online-updating regularized kernel matrix factorization models for large-scale recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
On prediction using variable order Markov models
Journal of Artificial Intelligence Research
VOGUE: A variable order hidden Markov model with duration based on frequent sequence mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Personalized news recommendation based on click behavior
Proceedings of the 15th international conference on Intelligent user interfaces
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Ontology-based news recommendation
Proceedings of the 2010 EDBT/ICDT Workshops
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
Factorizing personalized Markov chains for next-basket recommendation
Proceedings of the 19th international conference on World wide web
Online evolutionary collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Using temporal data for making recommendations
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
A universal data compression system
IEEE Transactions on Information Theory
Workshop and challenge on news recommender systems
Proceedings of the 7th ACM conference on Recommender systems
PEN RecSys: a personalized news recommender systems framework
Proceedings of the 7th ACM conference on Recommender systems
PEN recsys: a personalized news recommender systems framework
Proceedings of the 2013 International News Recommender Systems Workshop and Challenge
Personalized news recommendation based on implicit feedback
Proceedings of the 2013 International News Recommender Systems Workshop and Challenge
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The proliferation of online news creates a need for filtering interesting articles. Compared to other products, however, recommending news has specific challenges: news preferences are subject to trends, users do not want to see multiple articles with similar content, and frequently we have insufficient information to profile the reader. In this paper, we introduce a class of news recommendation systems based on context trees. They can provide high-quality news recommendations to anonymous visitors based on present browsing behaviour. Using an unbiased testing methodology, we show that they make accurate and novel recommendations, and that they are sufficiently flexible for the challenges of news recommendation.