For each of these steps I needed some basic computer skills, but I never needed to write computer code. What was most essential for me to provide was the knowledge of whiskey and an opinion about which type of whiskey was the best choice in different contexts.
Since I wasn't an actual whiskey expert, I had to use the internet to educate myself. I also realized, too late, that I had taken on a really, really complicated subject for what was supposed to be a simple tutorial! This forced me to make several compromises between completeness and manageability. I had to cut corners. Each time I did, I noted that if I were building a real application I might have made different choices, and suggested how a more complete approach might be pursued.
So what did my efforts get me? Here are some screenshots of Deb's Whiskey Recommender, the app generated by the Recommender Apperator:
The logic I embedded in the Context Decision Tree is transformed into a simple wizard.
As the user makes choices, those choices are placed into the 'breadcrumbs' at the top of the page. This navigation device not only makes it clear which path has been taken through the tree, it makes it simple to backtrack to any one of the decisions and change it. When a user clicks on a prior choice, he or she will be taken to that spot in the wizard.
From a 'Shape of Knowledge' perspective, it is worth noting that the Context Decision knowledge is captured in a triangle shape, a tree, but the user interface serves that knowledge up in a linear fashion. This is an example of one of the best practices I talk about in my book: use the best knowledge shape for capturing the knowledge, but use the simplest shape possible when presenting it to a user. In this case, the triangular knowledge is flattened to a line by the wizard-like UI.
Based on this particular set of decisions, a long list of whiskeys is recommended. This path takes the user to the bourbons, both Tennessee Whiskey and Kentucky Straight Bourbon. Here is a sample of the recommended list:
The baseball card format lays out information from the Options Table. Shown are three of the many Kentucky Straight Bourbons in the Whiskey Table. At this point, the implications of the compromises I made become clear: everything on these three cards is identical, except for the brand name. Because all three whiskeys are from the same manufacturer, they have the same image. Because they are all the same type of whiskey, they have the same taste profile. Even though the price levels were calculated individually, they all happen to fall into the same price group. It would have been nicer if I had documented this information down to the individual brand level, but with 450 brands in the data table that wasn't feasible.
The options for knowledge authors building future Recommenders are these: pick a simpler topic, with fewer options; crowd-source the information in the Options Table; or be prepared to do a whole lot of work yourself, which is worth it if you are creating a Recommender to sell, establish your expertise, or to create competitive advantage.
Meanwhile, it is important to remember that the amount of effort required to create a complete data table is minor compared to the effort required to build this sort of application from scratch! The data has to be there, in any case -- there's no avoiding that work. But the ease of building the decision logic in KnowtShare is unparalleled, and the user interface and the computer logic that knits the pieces together comes for free.
This demo application achieved my goals: to document the process of building a Recommender, and to demonstrate that a potentially sophisticated application can be created based solely on domain expertise, no computer code required.
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