I will describe some of the data models that we use and discuss our approach to algorithmic innovation that combines offline machine learning with online testing. For this task we use distributed computing since we deal with over with 140 thousand users. MapReduce is a powerful technique and we use it by writting in python code with the framework MrJob.
I will present how can we use map-reduce programming model for making recommendations. Recommender systems are quite popular among shopping sites and social network thee days. How do they do it ? Generally, the user interaction data available from items and products in shopping sites and social networks are enough information to build a recommendation engine using classic techniques such as Collaborative Filtering.
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Creating engaging newsletters with your curated content is really easy.