Real-Time News Recommendation Using Context-Aware Ensembles

  • Andreas Lommatzsch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)


With the rapidly growing amount of items and news articles on the internet, recommender systems are one of the key technologies to cope with the information overload and to assist users in finding information matching the their individual preferences. News and domain-specific information portals are important knowledge sources on the Web frequently accessed by millions of users. In contrast to product recommender systems, news recommender systems must address additional challenges, e.g. short news article lifecycles, heterogonous user interests, strict time constraints, and context-dependent article relevance. Since news articles have only a short time to live, recommender models have to be continuously adapted, ensuring that the recommendations are always up-to-date, hampering the pre-computations of suggestions. In this paper we present our framework for providing real-time news recommendations. We discuss the implemented algorithms optimized for the news domain and present an approach for estimating the recommender performance. Based on our analysis we implement an agent-based recommender system, aggregation several different recommender strategies. We learn a context-aware delegation strategy, allowing us to select the best recommender algorithm for each request. The evaluation shows that the implemented framework outperforms traditional recommender approaches and allows us to adapt to the specific properties of the considered news portals and recommendation requests.


real-time recommendations online evaluation context-aware ensemble 


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  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extentions. IEEE Transactions on Knowledge and Data Engineering 17(6) (2005)Google Scholar
  2. 2.
    Albayrak, S., Wollny, S., Lommatzsch, A., Milosevic, D.: Agent technology for personalized information filtering: The pia-system. Scalable Computing: Practice and Experience 8 (2007)Google Scholar
  3. 3.
    Bell, R., Koren, Y.: Lessons from the netflix prize challange. ACM SIGKDD Explorations 9(2), 75–79 (2007)CrossRefGoogle Scholar
  4. 4.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  5. 5.
    Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, IUI 2010, pp. 31–40. ACM, New York (2010)Google Scholar
  6. 6.
    Polikar, R.: Ensemble based systems in decision making. IEEE Circuits and Systems Magazine 6(3), 21–45 (2006)CrossRefGoogle Scholar
  7. 7.
    Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: Proc. of the 5th ACM Conf. on Recommender Systems, RecSys 2011. ACM, NY (2011)Google Scholar
  8. 8.
    Research, G.: Movielens data sets (October 2006),
  9. 9.
    Scheel, C., Neubauer, N., Lommatzsch, A., Obermayer, K., Albayrak, S.: Efficient query delegation by detecting redundant retrieval strategies. In: Proceedings of SIGIR 2007 Workshop: Learning to Rank for Information Retrieval (2007)Google Scholar
  10. 10.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. In: Advances in Artificial Intelligence (January 2009)Google Scholar
  11. 11.
    Tan, A.-H., Teo, C.: Learning user profiles for personalized information dissemination. In: Proc. of the IEEE World Congress on Comp. Intelligence, vol. 1, pp. 183–188 (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Andreas Lommatzsch
    • 1
  1. 1.DAI-LabTechnische Universität BerlinBerlinGermany

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