Real-Time News Recommendation Using Context-Aware Ensembles

  • Andreas Lommatzsch
Conference paper

DOI: 10.1007/978-3-319-06028-6_5

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)
Cite this paper as:
Lommatzsch A. (2014) Real-Time News Recommendation Using Context-Aware Ensembles. In: de Rijke M. et al. (eds) Advances in Information Retrieval. ECIR 2014. Lecture Notes in Computer Science, vol 8416. Springer, Cham

Abstract

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.

Keywords

real-time recommendations online evaluation context-aware ensemble 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

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

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