Literature Review with Study and Analysis of the Quality Challenges of Recommendation Techniques and Their Application in Movie Ratings

During the past few decades, the web-based services and organizations like Amazon, Netflix and YouTube have been raised aggressively. These web services have shown the demand for the recommender systems and their growing place in our lives. More steps deeper, we noticed that the severity of the quality and accuracy of these recommendation systems is very high to match users with same interests. For that reason and for being in competitive position with the most outstanding recommendation web services, these recommendation systems should be always monitored and evaluated from a quality perspective. However, due to the steep growth rate of the available web-based services, new challenges like data sparsity, scalability problem and cold start issue have been burst and threaten the performance and the quality of the predicted recommendations. Accordingly, many data scientists and researchers got excited to figure out ways for these challenges especially if they are in scaled environments and distributed systems. These solutions could be achieved using multiple approaches such as machine learning and data mining.