In this talk we will go over some recent developments in the theory of sequential prediction drawing parallels to statistical learning theory. Then we will see how the theory can be used to provide a general recipe to design algorithms for sequential prediction problems. As example we will see how the theory and algorithm design recipe can lead to interesting randomized algorithms for sequential collaborative filtering and sequential link prediction in signed social networks.