International Journal of Education and Management Engineering(IJEME)

ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)

Published By: MECS Press

IJEME Vol.7, No.2, Mar. 2017

Research Work Area Recommendation based on Collaborative Filtering

Full Text (PDF, 789KB), PP.50-62

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Richa Sharma, Sharu Vinayak, Rahul Singh

Index Terms

Collaborative filtering;Cosine similarity;Tanimoto coefficient;Recommender systems


In this work we present RWARS, a novel recommender system that recommends research work area. So far a number of recommender systems have been developed in the field of e-commerce, e-services, e-library, entertainment, tourism and social networking sites. However, when it comes to the area of education, not much work has been done. So to extend the utility of Recommender systems in the field of education, we have developed RWARS. We have used Cosine similarity and Tanimoto coefficient for developing our system. The aim of this work is to compare the results obtained using each approach to find the most optimal one. Evaluation parameters that have been used are: Mean square error, Root mean square error and Coverage. At present, RWARS is still in its initial phase and its applicability can be further enhanced by converting it into an online system and it surely will prove to be a great boon for young researchers to select the most appropriate research area for them.

Cite This Paper

Richa Sharma, Sharu Vinayak, Rahul Singh,"Research Work Area Recommendation based on Collaborative Filtering", International Journal of Education and Management Engineering(IJEME), Vol.7, No.2, pp.50-62, 2017.DOI: 10.5815/ijeme.2017.02.06


[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]Yazdanfar, Nazpar, and Alex Thomo. "Link recommender: Collaborative-Filtering for recommending URLS to Twitter users." Procedia Computer Science 19 (2013): 412-419.

[7]Zahra, Sobia, Mustansar Ali Ghazanfar, Asra Khalid, Muhammad Awais Azam, Usman Naeem, and Adam Prugel-Bennett. "Novel centroid selection approaches for KMeans-clustering based recommender systems." Information Sciences 320 (2015): 156-189.

[8]Kothari, Aansi A., and Warish D. Patel. "A Novel Approach Towards Context Based Recommendations Using Support Vector Machine Methodology." Procedia Computer Science 57 (2015): 1171-1178.

[9]De Nart, Dario, and Carlo Tasso. "A personalized concept-driven recommender system for scientific libraries." Procedia Computer Science38 (2014): 84-91.

[10]Dutta, P. and Kumaravel, A., 2016. A Novel Approach to Trust based Identification of Leaders in Social Networks. Indian Journal of Science and Technology, 9(10).

[11]Reddy, C.A. and Subramaniyaswamy, V., 2015. An Enhanced Travel Package Recommendation System based on Location Dependent Social Data. Indian Journal of Science and Technology, 8(16), p.1.

[12]Singh, G., Boparai, R.S. and Kathpal, M., 2016. A Novel Hybrid K-Means PLSA Technique for Music Recommender. Indian Journal of Science and Technology, 9(16).

[13]Verbert, Katrien, Hendrik Drachsler, Nikos Manouselis, Martin Wolpers, Riina Vuorikari, and Erik Duval. "Dataset-driven research for improving recommender systems for learning." In Proceedings of the 1st International Conference on Learning Analytics and Knowledge, pp. 44-53. ACM, 2011.

[14]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.

[15]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.

[16]Wang, Pu. "A Personalized Collaborative Recommendation Approach Based on Clustering of Customers." Physics Procedia 24 (2012): 812-816.

[17]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.

[18]Perone, C. S. "Machine Learning: Cosine Similarity for Vector Space Models (Part III). Pyevolve." Availa ble: http://pyevolve. sourcefor ge. net/wordpress.

[19]Wikipedia contributors, "Jaccard index," Wikipedia, The Free Encyclopedia, May 25, 2016).