Abstract: Online social networks are experiencing an explosive growth in recent years in both the number of users and the amount of information shared. The users join these social networks to connect with each other, share, find content and disseminate information by sending short text messages in near real-time. As a result of the social networks, the users are often experiencing information overload since they interact with many other users and read ever increasing content volume. Recommendation systems have been proposed to help users cope with information overload by predicting the items that a user may be interested in. The user's preferences are shaped by personal interests. At the same time, users are affected by their surroundings, as determined by their geographically located communities. One of the approach takes into account both personal interests and local communities. A new dynamic recommendation system model that provides better customized content to the user is described provides the user with the most important tweets according to his individual interests. Study of how changes in the geographical community preferences can affect the individual userís interests is done through this. These community preferences are generally reflected in the localized trending topics.
Keywords: Recommendation systems, social networks, topics modeling, trending topics etc.