Adding feedback loop to your applications

These days everyone is talking about smart applications.

One very simple feature that smart applications have is measuring user interactions.
Learning from user interactions and acting in a more personalized manner towards the user.

User interaction can mean different things for different applications. Here i am going to talk about one popular user interaction metric.

"Time spent by a user on a given feature"

Assuming you have some way of measuring the time spent by a user on a given feature. You can calculate the average time spent by a user and compare it with the time spent by other realistic users by using some statistics.

Assuming you have a list of users and their corresponding time's spent on the feature.
You can calculate the mean time for the list.

Lets call this mean_time.

You can also calculate the standard deviation for the list.

Lets call this time_range_delta

you actual time_range interval is then (mean_time - (time_range_delta * range_factor), mean_time + (time_range_delta * range_factor))

where range factor is the amount spurious information you are will to tolerate a range of [1-3] for this factor is generally considered good.

once you have this range interval, you can ignore all times that fall outside this range interval.

this will give a new list, from which you can calculate the mean and standard deviation again.

By following this process iteratively you can get the actual list of genuine users and their corresponding times.

Calculating the mean of this list will give the average time spent by the user.

You can then classify any user by using this average time.