I have the following problem:A company, let's call it X, has a job-adds website. It works as a marketplace where job-seekers and companies that have job vacancies can meet. Their business model is such that job-seekers pay a subscription to get access to the website after a trial period. Companies, on the other hand, don't pay anything to post job-adds. In order to attract companies to post jobs on the website they call them and ask if they're interested. They have a database with N companies that did business with them in the past. Out of those N they can only call 20% due to budget and personnel constraints. They select those 20% out of simple heuristic: companies that posted job adds in the last six months.What they want is to call companies that have high probability of posting a job add that will generate a match. They consider that a match has happened based on a proxy: the company contacted n different candidates for a particular job-add. I want to find a data-driven approach to select the best companies for them to call. In other words, provide them with a list of companies that have a high probability of generating adds that match with their current database of users.The problem I'm having is how to transform this business problem into a machine learning solution. What I have thought so far is to build a classification model. Each row is a job-add. The features are variables related to the company (size, location), to the user (age, CV, last time logged in), and the job-add itself (salary,education required, position, industry). The target variable is a binary of the job had a mach or not. The problem with my approach is that the model is not actually learning anything since my target variable is already known for each job-post (I know which job-adds had a match and which didn't). I'm looking for some ideas on how I can structure this problem in a data-driven framework. Should I continue with my classification model? Maybe some type of collaborative filtering? I'm sure machine learning can provide better results than their simple heuristic. Thanks!The data I have:User data, including CV;Who applied to what job and when;Data from the companies that posted job-adds;Contacts made by companies to job-seekers (This just tells me if the company clicked a button to look at the user's contact info);
Translating a business problem into a machine learning solution: job-adds website
machine learning;data mining;recommender system