- We’ll begin with what all data-driven applications should start with—analytics.
- I’ll attempt to convince you of the great value of analytics, and we’ll look at how to implement an analytics dashboard.
- I’ll introduce personas and why they’re useful.
- Using these personas, you’ll learn different ways to represent user taste.
In the previous chapters, you learned what a recommender system produces and what you can learn from users visiting a site. At this point, you should understand what you want to achieve and what evidence you’ll need to do that. Now you’re missing only the two parts in the middle as shown in figure 3.1.
Figure 3.1. Data flow from evidence to recommendations. You start with your evidence, which you can aggregate into website usage. With that, you can start to understand the user’s tastes, which can work as input to the recommender system to produce recommendations.
To understand the two middle steps, you need to find a way to understand what your users are up to. Taking the log data that the collector described in the previous chapter, you now need to find a way to reduce the data for each user to a preference. To do this, there are scripts that auto-generate interactions, which will provide the data that I’ve based the discussion on. Then you’re going to learn about simple analytics, something that you can set up and continuously view.