RECOMMENDATION ENGINE FOR COMPETITIVE CODING QUESTIONS USING RESTRICTED BOLTZMANN MACHINES, A HYBRID APPROACH
Mohammed Arshad Siddiqui · International Journal of Advanced Research in Computer Science · 2018
Recommendation engines have made a massive impact on every major online platform ranging from social networking to e-commerce. Recommender engines are software applications that help users by giving personalized suggestions on the services or products that are offered. They are responsible of finding relations between the provided products or services based on their inherent complementary nature of items and according to the crowd popularity. One such domain where these recommendation systems are yet to make their mark, is the area of competitive coding. Competitive coding has become a major sport and selection criteria for many organizations for their candidate selection. The users engage with these websites and portals to gain valuable problem-solving skills and improve their programming abilities. Here we have presented a recommendation system for such organizations. Our approach uses vectors of weights using vector space model and TF-IDF weighting scheme for the questions. These weights are used in an unsupervised collaborative filtering process achieved using undirected graphical models, called Restricted Boltzmann Machines (RBMs) and then using the generated probabilities to predict the best questions for the users. We present efficient learning and inference procedures and demonstrate that RBM’s can be successfully applied to a large data set containing tags of questions solved by the users.