Main Article Content

Arshad Mohammed Siddiqui
Harsh Agarwal


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.


Download data is not yet available.

Article Details

Author Biographies

Arshad Mohammed Siddiqui, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur

Under Graduate Student Department of Computer Science and Engineering

Harsh Agarwal, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur

Under Graduate Student Department of Electronics and Communication Engineering


Goldberg, David, David Nichols, Brian M. Oki, and Douglas Terry. "Using collaborative filtering to weave an information tapestry." Communications of the ACM 35, no. 12 (1992): 61-70.

Terveen, Loren, and Will Hill. "Beyond recommender systems: Helping people help each other." HCI in the New Millennium 1, no. 2001 (2001): 487-509.

Sarwar, Badrul, George Karypis, Joseph Konstan, and John Riedl. "Item-based collaborative filtering recommendation algorithms." In Proceedings of the 10th international conference on World Wide Web, pp. 285-295. ACM, 2001.

Ramos, Juan. "Using tf-idf to determine word relevance in document queries." In Proceedings of the first instructional conference on machine learning, vol. 242, pp. 133-142. 2003.

Das, Abhinandan S., Mayur Datar, Ashutosh Garg, and Shyam Rajaram. "Google news personalization: scalable online collaborative filtering." In Proceedings of the 16th international conference on World Wide Web, pp. 271-280. ACM, 2007.

Salakhutdinov, Ruslan, Andriy Mnih, and Geoffrey Hinton. "Restricted Boltzmann machines for collaborative filtering." In Proceedings of the 24th international conference on Machine learning, pp. 791-798. ACM, 2007.

Larochelle, Hugo, and Yoshua Bengio. "Classification using discriminative restricted Boltzmann machines." In Proceedings of the 25th international conference on Machine learning, pp. 536-543. ACM, 2008.

Rokach, Francesco Ricci Lior. "Introduction to Recommender Systems Handbook Francesco Ricci Lior Rokach And Bracha Shapira."

Melville, Prem, and Vikas Sindhwani. "Recommender systems." In Encyclopedia of machine learning, pp. 829-838. Springer US, 2011.

Hinton, Geoffrey E. "A practical guide to training restricted Boltzmann machines." In Neural networks: Tricks of the trade, pp. 599-619. Springer, Berlin, Heidelberg, 2012.

Beel, Joeran, Stefan Langer, Marcel Genzmehr, and Andreas Nürnberger. "Introducing Docear's research paper recommender system." In Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries, pp. 459-460. ACM, 2013.

Mikolov, Tomas, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. "Distributed representations of words and phrases and their compositionality." In Advances in neural information processing systems, pp. 3111-3119. 2013.

Hongliang, Cui, and Qin Xiaona. "The video recommendation system based on DBN." In Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/ IUCC/ DASC/ PICOM), 2015 IEEE International Conference on, pp. 1016-1021. IEEE, 2015.