In addition, the vocabulary of the representation should be designed so the relevant distinctions needed to predict user behavior are represented in a way that generalizes across different contexts. The representation should include both objective and subjective information about user interactions in order to maximize the opportunity to predict user behavior, and thereby adapt to their individual needs. Profile information supplied by users is only a small portion of the objective information that should be represented for the purpose of learning predictive models. Much of this information is often encoded implicitly in the internal structure of an interactive service or product. It must be represented explicitly in the form of data that can be analyzed by learning algorithms in order for learning to even be possible.
Subjective information is also important for predicting user behavior.
Two people can respond quite differently to the same presentation. The challenge is to predict differences in response based on what is known about each person. Although the objective content of a presentation is an important component of such a prediction, one must also consider the subjective aspects of the presentation and how those aspects relate to individual users. For example, a group of data analysts I know were working with a direct mail retailer and were attempting to determine why some of the retailer's catalogs generated more sales than others, even though the contents of the catalogs were very similar. One of the analysts noticed differences in the visual appeal of each catalog.