On Measuring the Role of Social Networks in Project Recommendation
DOI:
https://doi.org/10.26438/ijcse/v6i4.215219Keywords:
Recommender system, Social networks, Collaborative learningAbstract
With the emergence of Internet technology, users have started exploring, connecting and socializing themselves on the social media anywhere and anytime. Social networks have reformed the means we communicate. Online social networks are gaining importance due to the generation of large metadata that was never possible before. With this metadata from social networks, recommender systems gain benefit to determine rating preferences of users. Nowadays, social networks are also becoming useful in academics. They promote collaborative learning between students. This paper inspects the role of social networks in recommending projects to students. We propose a system that uses social network information of students to generate recommendations. We use several factors which play essential role in project recommendations. The contextual information from user profiles and the tags that are used by projects for reviewing, rating, tagging or contributing are employed. These tags are then used to extract the most relevant tags on the basis of the factors considered.
References
P. Dillenbourg, “Collaborative learning: Cognitive and computational approaches, ” Elsevier, 1999.
F. Ricci, L. Rokach, B. Shapira, “Recommender Systems: Introduction and Challenges,” Springer, pp. 1–34, 2015.
P. Melville, V. Sindhwani, “Recommender systems,” in Encyclopedia of Machine Learning, Springer US, pp. 829–838, 2010.
M. Kohar, C. Rana, “Survey Paper on Recommendation System,” International Journa of Computer Science and Information Technology, Vol.3, Issue.2, pp. 3460–3462, 2012.
N.H.M. Alwi, N. A. Mahir, S. Ismail, “Infusing Social Media in Teaching and Learning (TnL) at Tertiary Institutions: A Case of Effective Communication in Universiti Sains Islam Malaysia (USIM),” Procedia - Social and Behavioral Sciences., Vol.155, Issue.October, pp. 265–270, 2014.
J. Shokeen, P. Yadav, Meenakshi, “Community detection in social networks,” Journal of Emerging Technologies and Innovative Research, Vol.3, Issue.8, pp. 32–34, 2016.
I. Guy, D. Carmel, “Social recommender systems,” in Recommender Systems Handbook, Springer, pp. 511–543, 2015.
P. Rani, J. Shokeen, “Issues and Challenges in Link Prediction for Social Networks,” In the Proceedings of the 11th INDIACom and 4th International Conference on Computing for Sustainable Global Development, India, pp. 6889–6895, 2017.
M. Ali, R. A.I.B.R. Yaacob, M.N.A.B. Endut, N.U. Langove, “Strengthening the academic usage of social media: An exploratory study,” Journal of King Saud University - Computer and Information Sciences, Vol. 29, Issue.4, pp. 553–561, 2017.
W.M. Al-Rahmi, M.S. Othman, M.A. Musa, “The Improvement of Students’ Academic Performance by Using Social Media through Collaborative Learning in Malaysian Higher Education,” Asian Social Science, Vol.10, Issue.8, pp. 210–221, 2014.
C. Rana, S.K. Jain, “Building a book recommender system using time based content filtering,” WSEAS Trans. Comput., Vol. 11, Issue.2, pp. 27–33, 2012.
S. Garcia-Martinez, A. Hamou-Lhadj, “Educational Recommender Systems: A Pedagogical-Focused Perspective,” in Multimedia Services in Intelligent Environments. Smart Innovation, Systems and Technologies, Vol.25, G. A. Tsihrintzis, M. Virvou, and L. C. Jain, Eds. Heiderlberg: Springer, 2013, pp. 113–124.
T.Y. Tang, G. McCalla, “A multidimensional paper recommender: Experiments and evaluations,” IEEE Internet Comput.ing, Vol.13, Issue.4, pp. 34–41, 2009.
M.M. Recker, A. Walker, K. Lawless, “What do you recommend? Implementation and analyses of collaborative information filtering of web resources for education,” Instructional Science, Vol.31, Issue.4–5, pp. 299–316, 2003.
E. Seralidou, C. Douligeris, “Identification and Classification of Educational Collaborative Learning Environments,” Procedia of Computer Science, Vol.65, no. Iccmit, pp. 249–258, 2015.
J. Shokeen, C. Rana, “A study on Trust-aware Social Recommender Systems,” In the Proceedings of the 12th INDIACom and 5th International Conference on Computing for Sustainable Global Development, India, pp. 4268–4272, 2018.
P. Sharma, R.K. Gupta, “A Novel Web Usage Mining Technique Analyzing User Behaviour Using Dynamic Web Log,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.106-111, 2017.
C. Rana, S.K. Jain, “A study of the dynamic features of recommender systems,” Artificial Intelligence Review, Vol.43, Issue.1, pp. 141–153, 2012.
S. Guha, R. Rastogi, K. Shim, “Rock: A robust clustering algorithm for categorical attributes,” Information Systems, Vol. 25, Issue.5, pp. 345–366, 2000.
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