How to quantify a tokenized user agent string for a neural network? I am currently experimenting with user agent strings. My current plan is to tokenize the user agent string and split it in its important parts. Like OS, Browser Name, etc.

After that, does it make sense to just create some sort of map where iOS = 0.1 and Windows = 0.2 where i map the strings to numbers for the operating system feature of my network?

Is that the current best approach to work with such data, or should I try a different approach? The network should predict which category the user will click, and than move that category to a more prominent place.

For example, I expect that an iOS user prefers an iPhone mobile case more than an android one.

Thank you for helping with my experiments.
iOS, Windows and Android should not be viewed as strings but as categories. Categories should be one-hot encoded, and should not share the same input as they are not related to each other.

So let's assume we work with two operating systems: iOS and Windows.

So for iOS, input is: 1 0 and for Windows input is: 0 1.

But now we want to add another OS: Android.

iOS becomes: 1 0 0, Windows 0 1 0, Android: 0 0 1. Note that by adding android, you do not actually change the input! You only add an extra 0 for iOS and Windows, and 0's don't get computed in neural networks 0*weight = 0. 

So by adding an OS, you do not affect what the network has learned for the previously trained OS's.