RECOMMENDATION ENGINE FOR COMPETITIVE CODING QUESTIONS USING RESTRICTED BOLTZMANN MACHINES, A HYBRID APPROACH

Article Sidebar

PDF
Published: Feb 20, 2018
DOI: https://doi.org/10.26483/ijarcs.v9i1.5321
Keywords:
Neural Networks, Restricted Boltzmann Machines (RBM), Recommendation System, Collaborative Filtering, Competitive Coding, Unsupervised Learning

Main Article Content

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

Abstract

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.

Downloads

Download data is not yet available.

Article Details

Issue
Vol. 9 No. 1 (2018): January-February 2018
Section
Articles

COPYRIGHT

Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
  • The journal allows the author(s) to retain publishing rights without restrictions.
  • The journal allows the author(s) to hold the copyright without restrictions.
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

References

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.