[MapR Talk] Completely Real-time Recommendations

Document created by aalvarez on Dec 1, 2015Last modified by aalvarez on Dec 7, 2015
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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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