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Currently deployed recommendation technology almost always provides real-time recommendations based on a model that is developed using off-line techniques. The use of off-line training severely limits the ability to deal with fast changing content such as news or auctions.
I will describe techniques which make it possible to move this training load into true real-time without loss of accuracy. Real-time training of recommendations is rarely done, partly because existing algorithms require periodic off-line training to correct accumulating inaccuracies. The techniques that I will describe do not require off-line training of any kind.
In addition, the techniques described in this talk are easy to implement using stream processing, noSQL databases and search engines.
KEYWORDS: Machine Learning, Data Science, Recommendations, NoSQL.
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