IEEE Transactions on Knowledge and Data Engineering
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Personalized news recommendation based on click behavior
Proceedings of the 15th international conference on Intelligent user interfaces
Application of semantic annotations to predicting users' demographics
ESAIR '10 Proceedings of the third workshop on Exploiting semantic annotations in information retrieval
Workshop and challenge on news recommender systems
Proceedings of the 7th ACM conference on Recommender systems
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In this demo we present a robust system for delivering real-time news recommendation to the user based on the user's history of the past visits to the site, current user's context and popularity of stories. Our system is running live providing real-time recommendations of news articles. The system handles overspecializing as we recommend categories as opposed to items, it implicitly uses collaboration by taking into account user context and popular items and, it can handle new users by using context information. A unique characteristic of our system is that it prefers freshness over relevance, which is important for recommending news articles in real-world setting as addressed here. We experimentally compare the proposed approach as implemented in our system against several state-of-the-art alternatives and show that it significantly outperforms them.