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The next script is to extract customer details. We need customerid and productid as well as the ratings. In most commercial websites, recommendations are provided in order to increase revenue as well as customer satisfaction.
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After designing a recommender system in azure machine learning for a restaurant, let us use the typical adventureworksdatabase for a different recommender system. Every time you shop online, a recommendation system is guiding you towards the most likely product you might purchase. We have used the concat function to combine the customer name columns and bucketized the sales amount as shown in the below script. Let us see the experiment first and then let us examine the configurations.
