Recommender Systems and Analysis of Large-Scale Online Social Networks

People today see social networking as an essential part of business, communication, and getting information from one place to another. Each of these applications produces a lot of information sharing. Many large organizations spend a lot of time processing, analyzing, and looking for patterns before deciding. This research analyzes the different recommender systems used in online social networks that help organizations make better decisions since a vast amount of information is generated daily. When the number of social networks continues to grow, it is expected to have a recommender system that gives more accurate and reliable results. This research also aims to analyze and compare the different design challenges of using the recommender system in social network applications. This research looks at and compares different datasets for recommender systems to find problems and possible opportunities in how they are designed. The analysis and challenges of distinct large-scale online social networks with the heterogeneity of information and structures are also addressed in this research.