Friends recommendation on social networks

Social networks like Facebook and Twitter are prevailing nowadays. People use social networks to stay up-to-date with news and current events. Besides that, most people use social network so that they can make new friends. Friends recommendation on social networks is vital as it allow people to buil...

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Main Author: Rahman Syukri Othman
Other Authors: Mahardhika Pratama
Format: Final Year Project
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/76993
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-769932023-03-03T20:28:04Z Friends recommendation on social networks Rahman Syukri Othman Mahardhika Pratama School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering Social networks like Facebook and Twitter are prevailing nowadays. People use social networks to stay up-to-date with news and current events. Besides that, most people use social network so that they can make new friends. Friends recommendation on social networks is vital as it allow people to build better relationships as well as promoting information sharing and spreading. This project will investigate the friend recommendation problem for social networks. Unlike existing techniques like collaborative filtering that are widely utilized in recommender systems, this project will investigate the recommendation problem from the perspective of network science. To be specific, a social network will first be modeled as a temporal graph, then link prediction technique and its variants will be explored for calculating the probability for any pair of unconnected nodes to be connected. The recommendation methods include using evolutionary computations like Discrete Particle Swarm Optimization (DPSO) and Genetic Algorithm. Experiments on real-world social networks will be carried out to validate the effectiveness of the recommendation methods. Bachelor of Engineering (Computer Science) 2019-04-29T14:11:16Z 2019-04-29T14:11:16Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/76993 en Nanyang Technological University 45 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Rahman Syukri Othman
Friends recommendation on social networks
description Social networks like Facebook and Twitter are prevailing nowadays. People use social networks to stay up-to-date with news and current events. Besides that, most people use social network so that they can make new friends. Friends recommendation on social networks is vital as it allow people to build better relationships as well as promoting information sharing and spreading. This project will investigate the friend recommendation problem for social networks. Unlike existing techniques like collaborative filtering that are widely utilized in recommender systems, this project will investigate the recommendation problem from the perspective of network science. To be specific, a social network will first be modeled as a temporal graph, then link prediction technique and its variants will be explored for calculating the probability for any pair of unconnected nodes to be connected. The recommendation methods include using evolutionary computations like Discrete Particle Swarm Optimization (DPSO) and Genetic Algorithm. Experiments on real-world social networks will be carried out to validate the effectiveness of the recommendation methods.
author2 Mahardhika Pratama
author_facet Mahardhika Pratama
Rahman Syukri Othman
format Final Year Project
author Rahman Syukri Othman
author_sort Rahman Syukri Othman
title Friends recommendation on social networks
title_short Friends recommendation on social networks
title_full Friends recommendation on social networks
title_fullStr Friends recommendation on social networks
title_full_unstemmed Friends recommendation on social networks
title_sort friends recommendation on social networks
publishDate 2019
url http://hdl.handle.net/10356/76993
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