Abstract
Recommendation systems are recognised as being hugely important in industry as shown by Amazon and Netflix, and the area is now well understood. However, most recommendation systems are not optimised for the news room environment. At News UK, there is a requirement to be able to quickly generate recommendations for users on news items as they are published. However, little has been published about systems that can generate recommendations in response to changes in recommendable items and user behaviour in a very short space of time. In this paper we describe a new algorithm for updating collaborative filtering models incrementally, and demonstrate its effectiveness on clickstream data from The Times. We also describe the architecture that allows recommendations to be generated on the fly, now used in production for The Times and The Sun, and how we have made each component scalable.
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Many thanks to Dan Gilbert and Jonathan Brooks-Bartlett for feedback and support.
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Bailey, D., Pajak, T., Clarke, D., Rodriguez, C. (2017). Algorithms and Architecture for Real-Time Recommendations at News UK. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXIV. SGAI 2017. Lecture Notes in Computer Science(), vol 10630. Springer, Cham. https://doi.org/10.1007/978-3-319-71078-5_23
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DOI: https://doi.org/10.1007/978-3-319-71078-5_23
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