Self Organizing Networks (SON) aim at automating different network management functions, thereby improving their efficiency while reducing the operational expenditures. There are several proposed SON Functions (SFs) in the standards and a crucial one among them is Mobility Robustness Optimization (MRO). It focuses on providing seamless connectivity to mobile User Equipments (UEs). While optimizing handovers, there is a trade off between the Radio Link Failures (RLFs) and ping-pongs. Research has been widely done on the applicability of machine learning algorithms in SON for making decisions in a cognitive manner. In this study, MRO problem is modeled in two ways using two different classes of machine learning algorithms - Regression (linear and non-linear) and Recommender System. The work is evaluated on a Long Term Evolution (LTE) network simulator for different traffic scenarios. It is observed that the recommender system based solution has an edge over the regression based approaches and there is an overall improvement of 3.7% in the handover performance compared to that of the baseline approach.