Recommendation systems have been a common tool in today’s online world for finding items of interest to users. Common recommenders were knowledge-based, content-based, demographic-based, and collaborative filter recommenders. However, the best approach an organization could take in implementing a recommendation system was to create a hybrid recommender. The hybrid model was able to employ the advantages of the other recommenders and balanced out their disadvantages. Before an organization would implement a system, certain questions needed answering. Was the hybrid really the best approach at building a recommendation system? What challenges existed for the individual recommendation systems and did the hybrid approach resolve those challenges? How does an organization know if their recommendation system was accurate and providing quality recommendations? The paper’s objective was to answer these questions with a methodology of creating each of the individual recommenders as well as the hybrid recommender and investigating the results. From the findings, it was determined the hybrid model is the best approach, but not all challenges were overcome automatically with its implementation. To get the best results from a recommendation system, an organization needs to address those challenges. Once the challenges are acknowledged and addressed, then the recommendation system can provide the most accurate and meaningful results to its users.