Additionally, the distributor might subsidize the transmission in exchange for the rights to use any derived inferences about your movie preferences. Either way, the distributor knows what you have requested, and when you requested it, and so a little privacy is lost. The size of the transmission, and therefore the expense will soar. That is the price of privacy. The notion of a system that learns about its users, while an anathema to privacy, is necessary if our computers are to adapt to changes in our on-line behavior.
Applications can do whatever they wish with that value.
Ideally, user modeling systems would themselves choose which techniques to use. This calculation assumes that the strengths range between 0 and 1, and that the distribution across all users is uniform. In such cases, the strength is calculated in the same way, but the confidence calculation changes to reflect the additional information. Such a task would require recognizing a number of user patterns, such as when an event will next occur. Modeling events with linear prediction Linear prediction10 uses the autocorrelation of the data sequence to predict future values.