The Scoring Engine does a weighted learning from all different events giving higher scores to deeper interactions. In order to score the event property inside user memory, we either increase or decrease the magnitude of features variable of brand, category, collection, price-ranges with certain criteria. To score these property attributes, we have used FyndRank algorithm created by #Reco-team (inspired from EdgeRank of Facebook). Scoring ensures the features value stays within predefined ranges otherwise we normalize the score with the min-max method. Along with generating user memory, Stream Mapper also computes the similarity between brands and collections that can also recommended to users.