Collaborative Filtering (CF) is a method for predicting an individual’s next item selection, or recommending a collection of items from which to choose, by matching that individual’s profile of past item selections with the profile of like-minded individuals’ past item selections. For example, when one is buying my book Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data on Amazon.com, she is offered the recommendation “Customers who bought this item (book) also bought …. < A list of five books is given >.” Amazon is among the better known users of CF; but, there are many users across industry sectors, such as music, movies at the theater, video/CD movies, shoes (Zappos.com) and even online dating (JDate.com)! There are numerous commercial and non-commercial CF systems. However, they are all “black boxes.” Some CFs are called “propriety,” in a disingenuously effort of trying to be forthcoming. Regardless, unless the innards of CF systems are in the open, users and potential users will have uneasiness about CF-acceptance. The purpose of this article is to show the openwork of CF using SAS© procedures; thereby, revealing the secrets of the mystery CF to insure ease of acceptance and eagerness in its use.