Guide Me: A Research Work Area Recommender System

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Richa Sharma 1,* Sharu Vinayak 1 Rahul Singh 1

1. CSE Department, Chandigarh University,Gharuan, Mohali,140413, India

* Corresponding author.


Received: 22 Jan. 2016 / Revised: 1 Apr. 2016 / Accepted: 1 Jun. 2016 / Published: 8 Sep. 2016

Index Terms

Collaborative Filtering, Cosine Similarity, Recommender Systems


With the advent of Industrial Revolution, not only the choices in various fields increased but also the era of computer came into existence thereby revolutionizing the global market. People had numerous choices in front of them that often led to the confusion about what product might actually fulfill their requirements. So the need for having a system which could facilitate the selection criteria and eradicate the dilemma of masses, was realized and ultimately recommender systems of present day world were introduced. So we can refer recommender systems as software tools that narrow down our choices and provide us with the most suitable suggestions as per our requirements. In this paper, we propose a novel recommender system i.e. RWARS (Research Work Area Recommender System) that will recommend research work area to a user based on his/her characteristics similar to those of other users. The characteristics considered here are hobbies, subjects of interests, programming skills and future objectives. The proposed system will use Cosine Similarity approach of Collaborative Filtering.

Cite This Paper

Richa Sharma, Sharu Vinayak, Rahul Singh, "Guide Me: A Research Work Area Recommender System", International Journal of Intelligent Systems and Applications (IJISA), Vol.8, No.9, pp.30-37, 2016. DOI:10.5815/ijisa.2016.09.04


[1]Ricci, Francesco, Lior Rokach, and Bracha Shapira. Introduction to recommender systems handbook. Springer US, 2011.
[2]Jannach, Dietmar, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. Recommender systems: an introduction. Cambridge University Press, 2010.
[3]Ekstrand, Michael D., John T. Riedl, and Joseph A. Konstan. "Collaborative filtering recommender systems." Foundations and Trends in Human-Computer Interaction 4, no. 2 (2011): 81-173.
[4]Jannach, Dietmar, and Gerhard Friedrich. "Tutorial: recommender systems." In Proceedings of the International Joint Conference on Artificial Intelligence, Barcelona. 2011.
[5]Huttner, Joseph. "From Tapestry to SVD: A Survey of the Algorithms That Power Recommender Systems." (2009).
[6]Salehi, Mojtaba, Mohammad Pourzaferani, and Seyed Amir Razavi. "Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model." Egyptian Informatics Journal 14, no. 1 (2013): 67-78.
[7]Lee, Joonseok, Kisung Lee, and Jennifer G. Kim. "Personalized academic research paper recommendation system." arXiv preprint arXiv:1304. 5457(2013).
[8]Agarwal, Nitin, Ehtesham Haque, Huan Liu, and Lance Parsons. "Research paper recommender systems: A subspace clustering approach." InAdvances in Web-Age Information Management, pp. 475-491. Springer Berlin Heidelberg, 2005.
[9]Yazdanfar, Nazpar, and Alex Thomo. "Link recommender: Collaborative-Filtering for recommending URLS to Twitter users." Procedia Computer Science 19 (2013): 412-419.
[10]Wang, Pu. "A Personalized Collaborative Recommendation Approach Based on Clustering of Customers." Physics Procedia 24 (2012): 812-816.
[11]Hong, Kwanghee, Hocheol Jeon, and Changho Jeon. "Personalized Research Paper Recommendation System using Keyword Extraction Based on UserProfile." Journal of Convergence Information Technology 8, no. 16 (2013): 106.
[12]Moe, Hla Hla, and Win Thanda Aung. "Building Ontologies for Cross-domain Recommendation on Facial Skin Problem and Related Cosmetics."International Journal of Information Technology and Computer Science (IJITCS) 6, no. 6 (2014): 33.
[13]Abderrahim, Naziha, and Sidi Mohamed Benslimane. "Towards Improving Recommender System: A Social Trust-Aware Approach." International Journal of Modern Education and Computer Science (IJMECS) 7, no. 2 (2015): 8.
[14]Bousbahi, Fatiha, and Henda Chorfi. "MOOC-Rec: A Case Based Recommender System for MOOCs." Procedia-Social and Behavioral Sciences 195 (2015): 1813-1822.
[15]Liu, Haifeng, Zheng Hu, Ahmad Mian, Hui Tian, and Xuzhen Zhu. "A new user similarity model to improve the accuracy of collaborative filtering."Knowledge-Based Systems 56 (2014): 156-166.
[16]Nagpal, Diksha, Sumit Kaur, Shruti Gujral, and Amritpal Singh. "FR: A Recommender for Finding Faculty Based on CF Technique." Procedia Computer Science 70 (2015): 499-507.