The Journal of Machine Learning Research
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
The adaptive web
RecLab: a system for eCommerce recommender research with real data, context and feedback
Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
SCENE: a scalable two-stage personalized news recommendation system
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit
Proceedings of the fifth ACM conference on Recommender systems
MyMediaLite: a free recommender system library
Proceedings of the fifth ACM conference on Recommender systems
A live comparison of methods for personalized article recommendation at forbes.com
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Personalized News Recommendation Based on Collaborative Filtering
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Proceedings of the 22nd international conference on World Wide Web companion
Personalized news recommendation with context trees
Proceedings of the 7th ACM conference on Recommender systems
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We present the Personalized News (PEN) recommender systems framework, currently in use by a newspaper website to evaluate various algorithms for news recommendations. We briefly describe its system architecture and related components. We show how a researcher can easily evaluate different algorithms thanks to a web-based interface. Finally, we discuss important factors to take into account when conducting online evaluation, and report on our experience when deploying recommendations on a live-traffic website.