Community aware personalized hashtag recommendation in social networks

Research output: Chapter in Book/Conference paperConference paper

Abstract

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.

Original languageEnglish
Title of host publicationData Mining - 16th Australasian Conference, AusDM 2018
Subtitle of host publication Revised Selected Papers
EditorsYanchang Zhao, Graco Warwick, David Stirling, Chang-Tsun Li, Yun Sing Koh, Rafiqul Islam, Zahidul Islam
PublisherSpringer-Verlag Berlin
Pages216-227
Number of pages12
ISBN (Print)9789811366604
DOIs
Publication statusPublished - 1 Jan 2019
Event16th Australasian Conference on Data Mining, AusDM 2018 - Charles Sturt University, Bathurst, Australia
Duration: 28 Nov 201830 Nov 2018

Publication series

NameCommunications in Computer and Information Science
Volume996
ISSN (Print)1865-0929

Conference

Conference16th Australasian Conference on Data Mining, AusDM 2018
Abbreviated titleAusDM 2018
CountryAustralia
CityBathurst
Period28/11/1830/11/18

Fingerprint

Personalized Recommendation
Social Networks
Recommendations
Community Detection
Topology
Model
Community
Feature extraction
Social Structure
Interconnection
Feature Extraction
Predict

Cite this

Alsini, A., Datta, A., Huynh, D. Q., & Li, J. (2019). Community aware personalized hashtag recommendation in social networks. In Y. Zhao, G. Warwick, D. Stirling, C-T. Li, Y. S. Koh, R. Islam, & Z. Islam (Eds.), Data Mining - 16th Australasian Conference, AusDM 2018 : Revised Selected Papers (pp. 216-227). (Communications in Computer and Information Science; Vol. 996). Springer-Verlag Berlin. https://doi.org/10.1007/978-981-13-6661-1_17
Alsini, Areej ; Datta, Amitava ; Huynh, Du Q. ; Li, Jianxin. / Community aware personalized hashtag recommendation in social networks. Data Mining - 16th Australasian Conference, AusDM 2018 : Revised Selected Papers. editor / Yanchang Zhao ; Graco Warwick ; David Stirling ; Chang-Tsun Li ; Yun Sing Koh ; Rafiqul Islam ; Zahidul Islam. Springer-Verlag Berlin, 2019. pp. 216-227 (Communications in Computer and Information Science).
@inproceedings{e6b3faab50414bc3bf6585d4b44f0d9f,
title = "Community aware personalized hashtag recommendation in social networks",
abstract = "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.",
keywords = "Community detection, Hashtag recommendation, Social networks, Topics model, Twitter",
author = "Areej Alsini and Amitava Datta and Huynh, {Du Q.} and Jianxin Li",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/978-981-13-6661-1_17",
language = "English",
isbn = "9789811366604",
series = "Communications in Computer and Information Science",
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}

Alsini, A, Datta, A, Huynh, DQ & Li, J 2019, Community aware personalized hashtag recommendation in social networks. in Y Zhao, G Warwick, D Stirling, C-T Li, YS Koh, R Islam & Z Islam (eds), Data Mining - 16th Australasian Conference, AusDM 2018 : Revised Selected Papers. Communications in Computer and Information Science, vol. 996, Springer-Verlag Berlin, pp. 216-227, 16th Australasian Conference on Data Mining, AusDM 2018, Bathurst, Australia, 28/11/18. https://doi.org/10.1007/978-981-13-6661-1_17

Community aware personalized hashtag recommendation in social networks. / Alsini, Areej; Datta, Amitava; Huynh, Du Q.; Li, Jianxin.

Data Mining - 16th Australasian Conference, AusDM 2018 : Revised Selected Papers. ed. / Yanchang Zhao; Graco Warwick; David Stirling; Chang-Tsun Li; Yun Sing Koh; Rafiqul Islam; Zahidul Islam. Springer-Verlag Berlin, 2019. p. 216-227 (Communications in Computer and Information Science; Vol. 996).

Research output: Chapter in Book/Conference paperConference paper

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N2 - 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.

AB - 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.

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A2 - Warwick, Graco

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A2 - Li, Chang-Tsun

A2 - Koh, Yun Sing

A2 - Islam, Rafiqul

A2 - Islam, Zahidul

PB - Springer-Verlag Berlin

ER -

Alsini A, Datta A, Huynh DQ, Li J. Community aware personalized hashtag recommendation in social networks. In Zhao Y, Warwick G, Stirling D, Li C-T, Koh YS, Islam R, Islam Z, editors, Data Mining - 16th Australasian Conference, AusDM 2018 : Revised Selected Papers. Springer-Verlag Berlin. 2019. p. 216-227. (Communications in Computer and Information Science). https://doi.org/10.1007/978-981-13-6661-1_17