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An Experimental Evaluation of Link Prediction for Movie Suggestions Using Social Media Content
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Author(s): Anu Taneja (Jaypee Institute of Information Technology, India), Bhawna Gupta (Jaypee Institute of Information Technology, India)and Anuja Arora (Jaypee Institute of Information Technology, India)
Copyright: 2018
Pages: 28
Source title:
Social Network Analytics for Contemporary Business Organizations
Source Author(s)/Editor(s): Himani Bansal (Jaypee Institute of Information Technology, India), Gulshan Shrivastava (National Institute of Technology Patna, India), Gia Nhu Nguyen (Duy Tan University, Vietnam)and Loredana-Mihaela Stanciu (University Timisoara, Romania)
DOI: 10.4018/978-1-5225-5097-6.ch011
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Abstract
The enormous growth and dynamic nature of online social networks have emerged to new research directions that examine the social network analysis mechanisms. In this chapter, the authors have explored a novel technique of recommendation for social media and used well known social network analysis (SNA) mechanisms-link prediction. The initial impetus of this chapter is to provide general description, formal definition of the problem, its applications, state-of-art of various link prediction approaches in social media networks. Further, an experimental evaluation has been made to inspect the role of link prediction in real environment by employing basic common neighbor link prediction approach on IMDb data. To improve performance, weighted common neighbor link prediction (WCNLP) approach has been proposed. This exploits the prediction features to predict new links among users of IMDb. The evaluation shows how the inclusion of weight among the nodes offers high link prediction performance and opens further research directions.
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