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Community aware personalized hashtag recommendation in social networks

Version 2 2024-06-05, 02:22
Version 1 2019-05-06, 12:41
conference contribution
posted on 2024-06-05, 02:22 authored by A Alsini, A Datta, DQ Huynh, Jianxin LiJianxin Li
In the literature of social networks research, community detection algorithms and hashtag recommendation models have been studied extensively but treated separately. Community detection algorithms study the inter-connection between users based on the social structure of the network. Hashtag recommendation models suggest useful hashtags to the users while they are typing in their tweets. In this paper, we aim to bridge the gap between these two problems and consider them as inter-dependent. We propose a new hashtag recommendation model which predicts the top-y hashtags to the user based on a hierarchical level of feature extraction over communities, users, tweets and hashtags. Our model detects two pools of users: in the first level, users are detected using their topology-based connections; in the second level, users are detected based on the similarity of the topics of the tweets they previously posted. Our hashtag recommendation model finds influential users, reweighs their tweets, searches for the top-n similar tweets from the tweets pool of users who are socially and topically related. All hashtags are then extracted, ranked and the top-y are recommended. Our model shows better performance of the recommended hashtags than when considering the topology-based connections only.

History

Volume

996

Pagination

216-227

Location

Bathurst, N.S.W.

Start date

2018-11-28

End date

2018-11-30

ISSN

1865-0929

ISBN-13

9789811366604

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2019, Springer Nature Singapore Pte Ltd.

Editor/Contributor(s)

Islam R, Koh YS, Zhao Y, Warwick G, Stirling D, Li C-T, Islam Z

Title of proceedings

AusDM 2018 : Proceedings of the 16th Australiasian Data Mining Conference 2018

Event

Australian Data Mining. Conference (16th : 2018 : Bathurst, N.S.W.)

Publisher

Springer

Place of publication

Singapore

Series

Australian Data Mining Conference

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