Pair-Wise Trust Prediction Employing Matrix Factorization for Online Social Network
Rajeev Goyal1, Sanjiv Sharma2, Arvind Kumar Upadhyay3
1Rajeev Goyal, Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Madhya Pradesh, Maharajpura Dang, Gwalior (MP), India.
2Arvind K. Upadhyay, Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Madhya Pradesh, Maharajpura Dang, Gwalior (MP), India.
3Sanjiv Sharma, Department of Computer Science and Engineering, Madhav Institute of Technology and Science, Gwalior, Madhya Pradesh, India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 2686-2690 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7876068519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Online social networks become popular as a medium for propagating information and connecting like-minded people. The public accessibility of such networks with the ability to share opinions, thoughts, information, and experience offers great potential to enterprises and governments. In addition to individuals using such networks connect them to their friends and families, governments and enterprises and started exploiting these platforms for delivering their services to citizens and customers. However, the success of such attempts relies on the trust level with each other also with the service provider. Therefore, trust becomes an essential and important element of a successful social network. Matrix factorization is one of the state-of-the-art recommender systems. SDV and SDV+ are used for trust-based recommender system. SDV++ is used for both internal and external factors that affect trust. This paper proposed a novel method to predict trust by Novel SDV++ Matrix factorization techniques that use both propagation and latent factor approach to predict more accurate results.
Keywords: Online Social Network, Machine Learning, Matrix Factorization
Scope of the Article: Machine Learning