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Smart Information Systems pp 149–180Cite as

News Recommendation in Real-Time

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Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Recommender systems support users facing information overload situations. Typically, such situations arise as users have to choose between an immense number of alternatives. Examples include deciding what songs to listen to, what movies to watch, and what news article to read. In this chapter, we outline the case of suggesting news articles. This task entails a number of challenges. First, news collections do not remain relevant unlike movies or songs. Users continue to request novel contents. Second, users avoid creating consistent profiles thus reject login procedures. Third, requests arrive in enormous streams. Having short consumption times, users quickly request the next article to read. Handling these challenges requires adaptations to existing recommendation strategies as well as developing novel ones.

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Notes

  1. 1.

    https://github.com/plista/kornakapi/, https://github.com/plista/orp-sdk-java/.

  2. 2.

    https://github.com/plista/orp-sdk-php.

  3. 3.

    https://github.com/plista/contest-py/.

  4. 4.

    https://github.com/plista/contest-js/.

  5. 5.

    http://recsys.acm.org/recsys13/nrs/.

  6. 6.

    http://www.bars-workshop.org/.

  7. 7.

    http://www.clef-newsreel.org/.

  8. 8.

    http://hadoop.apache.org/.

  9. 9.

    https://spark.apache.org/.

  10. 10.

    https://storm.incubator.apache.org/.

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Acknowledgments

This work was funded by the Federal Ministry of Economic Affairs and Energy (BMWi) under funding reference number KF2392313KM2.

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Authors and Affiliations

  1. Technische Universität Berlin, Berlin, Germany

    Benjamin Kille & Andreas Lommatzsch

  2. Plista GmbH, Berlin, Germany

    Torben Brodt

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  1. Benjamin Kille
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Correspondence to Benjamin Kille .

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  1. Technische Universität Berlin, Germany

    Frank Hopfgartner

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Kille, B., Lommatzsch, A., Brodt, T. (2015). News Recommendation in Real-Time. In: Hopfgartner, F. (eds) Smart Information Systems. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-14178-7_6

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  • DOI: https://doi.org/10.1007/978-3-319-14178-7_6

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