There is a gap in defining the multi-criteria decision-making issues and with recommendation techniques and theories that can help develop the modulation coefficient recommenders. The main objective of this research is to identify an in-depth examination of the category of multiple variables recommendation systems. The current research focused on the group of multiple-criteria ranking recommenders' methods, which make recommendations by representing an individual's performance for the product as an ordered collection of rankings in addition to different parameters. The techniques used to make forecasts and produce recommendations using multi-criteria rankings are reviewed. In addition, we propose the multiple-criteria ranking algorithms. Experimental evaluations demonstrated that our proposed algorithms are able to solve the multi-criteria issues. Furthermore, the research consideration of unresolved problems and upcoming difficulties for the category of recommendations for multiple variables ratings.