# Which machine learning(recurrent/reinforcement learning) method/algorthim would suite this scenario

This application has it's roots in public transport, users opening the application and looking at the departure times of buses for specific stops (page 1) or planning a journey from location A to B with a journey planner(page 2). Two separate pages, two separate functions.

Page 1 you can say the user is familiar with the route, as they only need to know the departure and page 2 vice versa they do not know the route, and need a journey planner to assist them.

I am trying to make an application that displays one of the pages to the user depending on specific variables(which will make up a state). The page that the application shows to the user is dependent on former rewards on those specific states given the actions of the user; the user will either 1) stay on the page first shown, which will result in + reward or 2) navigate to the other page, which will result in - reward.

In simple terms, I would like to display the correct page to the user when the user enters the application, so the user does not have to navigate there themselves.

The features I get to play with include the following:

location
time of day
day of week
|----- monday
|----- tuesday
|----- wednesday
|----- thursday
|----- friday
|----- saturday
|----- sunday
week of month
|----- 1
|----- 2
|----- 3
|----- 4
month of year
|----- january
|----- february
|----- march
|----- april
|----- may
|----- june
|----- july
|----- august
|----- september
|----- october
|----- november
|----- december
action
|----- page 1
|----- page 2


An example of using this set would be the following:

The user enters the application and the application displays page 1 (default page). The user stays on the page, reward is given to the state.

{
location: '12th Example Street, Somecity',
timestamp: '2015-03-03 08:31:12', // <--- this includes day, week, month, year etc. as you can see
action: page1
}


As most commuters have 7-4 jobs it's very routine. Monday to Friday the user usually takes the bus to work every morning and then back at home. He knows his route so page 1 would be optimal to be displayed. This user on a non-specific Friday night however is out at a pub and has a pint too, ends up at an after party in the middle of who-knows-where resulting in trying to get home, page 2 would be optimal for him Saturday morning.

As learning goes, the application must learn before making assumptions, it must also respond to change in routine. A user might change job or move. It's safe to say that if the user does not know where they are that page 2 should be displayed, if the application is confident that the user knows where he is going page 1 is the better choice.

Now my question. Which algorithm/method would be best suited for this task, spending time on one just to realize that it was a complete waste is nothing but a total bummer. I've done supervised learning before however that won't cut it for obvious reasons.

Any comments on anything is welcome! I always like to better myself.