Towards context-aware personalization and a broad perspective on the semantics of news articles

Abstract

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 click-stream and postings data. Furthermore, we assess the impact of different recommendation strategies on the learning performance of our system. Keywords Algorithms, Human Factors, Languages. Copyright 2010 ACM.