Disclosed is a platform that makes use of hybrid model employing both heuristic and machine learning models to adaptively generate recommendations based on requested circumstances in a temporary staffing platform. The hybrid model is based on a set of training data surrounding historical temporary staffing outcomes. The heuristic model portion identifies matches between current queries to past outcomes and the machine learning model portion trains to derive new recommendations where no match exists. Queries are received and executed upon in real-time as opposed to pre-computing based on the frequency of changes to the recommendation to what would otherwise be the same query. The hybrid model is therefore configured to optimize for real-time responses to individual queries. The data surrounding the historical temporary staffing outcomes includes data relating to users, data relating to shifts, and data derived from a combination of both.