Muthukumar Kalaiselvan

Specialised coursework in specific areas of focus in Mobile and Social Application Programming,Information and communication technology ICT Systems Integration and Management,Computer security,Text Analytics, Natural Language Engineering,Professional Practice and Research Methodology and Physics-Based Games.

Dissertation: Leveraging User and Product Information in Review Classification
Abstract: In an attempt to develop more dependable movie recommendation algorithms, scholars and industry professionals have been studying movie rating prediction ever since the Netflix Movie Recommendation Challenge started. This work also covers the challenges and tactics involved in predicting movie ratings. The problem is tackled in this work by treating it as a combination recommendation and regression problem. Additionally, we explore several feature engineering methodologies and deliberate on the importance of retrieved features in rating prediction. We also performed a comparative study between our regression technique and the open-source recommendation system builder to evaluate the efficacy of our regression model trained on our retrieved attributes.