Recommender system on social networking site with domain specific and sparse data

In many recent domain-specific social networking sites, posts are organized in chronological order, where later posts are shown first at the top, even though they might not be of everyone's interest. As a result, if users want to read posts that interest them, they will have to scroll down and sift through all the posts. To overcome this information overload problem and relieve users' burden, a recommender system is needed in social networking sites. In this thesis we propose a hybrid approach of Recommender System (RS) that combines both Collaborative Filtering and Content-based approach. Although each approach has their own weaknesses independently, by joining them together we can improve the accuracy of our recommendations. From our experiments, we noticed that using learning to rank algorithms in combining each recommender algorithm greatly enhances the system's performance.