text: Authors describe a procedure of building their production recommender system from scratch, begining with formulating the recommendation problem, label data formation, model construction and learning.  They use several different evaluation techniques to show how successful their model is (offline metrics, A/B test results, etc.) Most of the originality comes from integrating time decay of purchases into the learning framework.  Rest of presented work is more or less standard. Paper may be useful to practitioners who are looking to implement something like this in production.