Which algorithm should be used for an accurate job recommendation system I'm building a testing project to get an introduction to DS &amp; ML. 
As a person part of the working force, sometimes finding a job is harder than it should be. I thought I could built a testing project to help workers find a job that best match their interests and their skills.  

I could use a classifier, but I do not know if regression if the best way to approach this as a first pass. I was thinking of a GA system that could find some way to find way to approach this over time in a X generation. Maybe this isn't the best way for recommending jobs.

What I'm looking for isn't the code for the problem but more of algorithms ideas I should take a look at and implement in any given programming language. I'm looking to implement a system that can take a peak at job descriptions, interests and skills of an individual. I think this doesn't sound too crazy to be able to train an agent and then make it look at every jobs there is on LinkedIn for instance and take a look at 'Software Engineer' and give me 45% matching and here's the three skills that you have to work on or 98% match, please apply.
You are much, much too early in your process to even begin thinking about your models. At this stage, you should be thinking about what the data looks like and how much of it do you need. For starters, what are you measuring? What is the answer that you seek? Is it job satisfaction? Likelihood to land an interview? Likelihood to get a job offer? 

Once you have that, how are you quantifying that? Is it a categorical variable? A continuous variable? Then you would have to decide on the likely list of factors that go into that answer and determine how you're going to get that data. Do you need to run a custom survey? Are there other datasets that you can leverage? You need to think about all of these answers before you're ready for any sort of modeling discussion.