Latent friends mining on social networks : a new friends recommendation system for Facebook
Latent friends of a person mean the persons who have the potential to become friends with the target person. Finding latent friends of a user on Online Social Networks (OSN) and recommending to the user is important for growing a user’s network. Users posts things happening to them on OSN to keep th...
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sg-ntu-dr.10356-592732023-03-03T20:53:29Z Latent friends mining on social networks : a new friends recommendation system for Facebook Zhang, Dong Qiu Lin Sun Aixin School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computer applications::Social and behavioral sciences Latent friends of a person mean the persons who have the potential to become friends with the target person. Finding latent friends of a user on Online Social Networks (OSN) and recommending to the user is important for growing a user’s network. Users posts things happening to them on OSN to keep their friends updated. It has been studied that the contents posted by Facebook users are expected to reflect their interests. Thus Facebook statuses can be used to identify one’s interests. In this report, the author proposes a friends recommendation system by exploiting Facebook user’s statuses. The system identifies topics from users’ statuses and classifies them into categories. Users are categorized based on the classifications of their topics. Similarity is calculated using the categorization results and the system recommends friends based on the similarity values. Experimental results using real Facebook users’ statuses provides the accuracy rate of the system and indicates the usefulness of the new friends recommendation system. Bachelor of Engineering (Computer Engineering) 2014-04-29T02:06:18Z 2014-04-29T02:06:18Z 2014 2014 Final Year Project (FYP) http://hdl.handle.net/10356/59273 en Nanyang Technological University 52 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computer applications::Social and behavioral sciences Zhang, Dong Latent friends mining on social networks : a new friends recommendation system for Facebook |
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Latent friends of a person mean the persons who have the potential to become friends with the target person. Finding latent friends of a user on Online Social Networks (OSN) and recommending to the user is important for growing a user’s network. Users posts things happening to them on OSN to keep their friends updated. It has been studied that the contents posted by Facebook users are expected to reflect their interests. Thus Facebook statuses can be used to identify one’s interests.
In this report, the author proposes a friends recommendation system by exploiting Facebook user’s statuses. The system identifies topics from users’ statuses and classifies them into categories. Users are categorized based on the classifications of their topics. Similarity is calculated using the categorization results and the system recommends friends based on the similarity values.
Experimental results using real Facebook users’ statuses provides the accuracy rate
of the system and indicates the usefulness of the new friends recommendation system. |
author2 |
Qiu Lin |
author_facet |
Qiu Lin Zhang, Dong |
format |
Final Year Project |
author |
Zhang, Dong |
author_sort |
Zhang, Dong |
title |
Latent friends mining on social networks : a new friends recommendation system for Facebook |
title_short |
Latent friends mining on social networks : a new friends recommendation system for Facebook |
title_full |
Latent friends mining on social networks : a new friends recommendation system for Facebook |
title_fullStr |
Latent friends mining on social networks : a new friends recommendation system for Facebook |
title_full_unstemmed |
Latent friends mining on social networks : a new friends recommendation system for Facebook |
title_sort |
latent friends mining on social networks : a new friends recommendation system for facebook |
publishDate |
2014 |
url |
http://hdl.handle.net/10356/59273 |
_version_ |
1759852951133749248 |