IEEE Transactions on Knowledge and Data Engineering
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
The Forgetron: A Kernel-Based Perceptron on a Budget
SIAM Journal on Computing
Analysis of Perceptron-Based Active Learning
The Journal of Machine Learning Research
Towards a context-sensitive online newspaper
Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
LOGO: a long-short user interest integration in personalized news recommendation
Proceedings of the fifth ACM conference on Recommender systems
Modeling and broadening temporal user interest in personalized news recommendation
Expert Systems with Applications: An International Journal
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We analyze preferences and the reading flow of users of a popular Austrian online newspaper. Unlike traditional news filtering approaches, we postulate that a user's preference for particular articles depends not only on the topic and on propositional contents, but also on the user's current context and on more subtle attributes. Our assumption is motivated by the observation that many people read newspapers because they actually enjoy the process. Such sentiments depend on a complex variety of factors. The present study is part of an ongoing effort to bring more advanced personalization to online media. Towards this end, we present a systematic evaluation of the merit of contextual and non-propositional features based on real-life clickstream and postings data. Furthermore, we assess the impact of different recommendation strategies on the learning performance of our system.