A decision making process is simplified with the help of recommender systems. Recommender systems process the knowledge sources and the information actively to collect data in order to build useful recommendations. These recommendations suggest suitable items to the user based on the analysis performed on the users preferences and constraints, both implicit and explicit. Using the content-based filtering approach of recommender systems, this article suggests an innovative idea to annotate the question asked by the user on a QA forum with suitable tags. This article presents a novel scheme to suggest the relevant tags by effectively analyzing questions from a clustered knowledge pool and then ranking the tags according to their relevance. This scheme aims at providing meaningful, trustworthy and persuasive recommendations which will stratify the question in the appropriate domain of a QA forum.