Enhancing user ranks in reputation system with splay tree by breaking power law

Abstract

 The reputation system is an efficient and effective way to build trust relationships among co-learners in
collaborative learning. In reputation system, users with higher point gets high rating compared to less scored users. As
per power law, drive alleged low users to the bottom of the ranking list. Break this law by encouraging low reputed users
in their early stage and preventing them from moving further down in ranking level. A splay tree is a Binary Search Tree
with self-balancing skill which brings the recently accessed item to the top of the tree. A splay tree represents user's
ranks. Low ranked users are semi-splayed in the tree thus preventing them from further drowning in the ranking list by
enhancing their ranks in the reputation system. In this paper, we find and enhance low scored users’ rank in reputation
system by providing few more chances to take part actively in the e-learning environment using splay tree and
normalized discounted cumulative gain (NDCG) which act as a decision part for identifying drowning users.