research-article

Weakly Supervised Attention for Hashtag Recommendation using Graph Data

Authors:

Kevin Chen-Chuan ChangAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 1038 - 1048

https://doi.org/10.1145/3366423.3380182

Published: 20 April 2020 Publication History

Abstract

Personalized hashtag recommendation for users could substantially promote user engagement in microblogging websites; users can discover microblogs aligned with their interests. However, user profiling on microblogging websites is challenging because most users tend not to generate content. Our core idea is to build a graph-based profile of users and incorporate it into hashtag recommendation. Indeed, user’s followee/follower links implicitly indicate their interests. Considering that microblogging networks are scale-free networks, to maintain the efficiency and effectiveness of the model, rather than analyzing the entire network, we model users based on their links towards hub nodes. That is, hashtags and hub nodes are projected into a shared latent space. To predict the relevance of a user to a hashtag, a projection of the user is built by aggregating the embeddings of her hub neighbors guided by an attention model and then compared with the hashtag. Classically, attention models can be trained in an end to end manner. However, due to the high complexity of our problem, we propose a novel weak supervision model for the attention component, which significantly improves the effectiveness of the model. We performed extensive experiments on two datasets collected from Twitter and Weibo, and the results confirm that our method substantially outperforms the baselines.

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Joe Dhanith PSaeed KRohith GRaja S(2024)Weakly supervised learning for an effective focused web crawlerEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.107944132:COnline publication date: 18-Jul-2024
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Al-Shaibani MLuqman HAl-Ghofaily AAl-Najim A(2024)WASM: A Dataset for Hashtag Recommendation for Arabic TweetsArabian Journal for Science and Engineering10.1007/s13369-023-08567-149:9(12131-12145)Online publication date: 3-Jan-2024
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Weakly Supervised Attention for Hashtag Recommendation using Graph Data
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cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

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Published: 20 April 2020

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Cited By

Bansal SGowda KKumar N(2024)Multilingual personalized hashtag recommendation for low resource Indic languages using graph-based deep neural networkExpert Systems with Applications10.1016/j.eswa.2023.121188236(121188)Online publication date: Feb-2024
https://doi.org/10.1016/j.eswa.2023.121188
Joe Dhanith PSaeed KRohith GRaja S(2024)Weakly supervised learning for an effective focused web crawlerEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.107944132:COnline publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.107944
Al-Shaibani MLuqman HAl-Ghofaily AAl-Najim A(2024)WASM: A Dataset for Hashtag Recommendation for Arabic TweetsArabian Journal for Science and Engineering10.1007/s13369-023-08567-149:9(12131-12145)Online publication date: 3-Jan-2024
https://doi.org/10.1007/s13369-023-08567-1
Feng KLiu TZhang HMeng ZMiao Z(2023)TNOD: Transformer Network with Object Detection for Tag RecommendationProceedings of the 2023 ACM International Conference on Multimedia Retrieval10.1145/3591106.3592246(617-621)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3591106.3592246
Huang ZSun YWang WSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Generalizing Graph ODE for Learning Complex System Dynamics across EnvironmentsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599362(798-809)Online publication date: 6-Aug-2023
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Al-Sabahi KYang K(2023)Supervised Copy Mechanism for Grammatical Error CorrectionIEEE Access10.1109/ACCESS.2023.329497911(72374-72383)Online publication date: 2023
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Sun ZFang HYang JQu XLiu HYu DOng YZhang J(2022)DaisyRec 2.0: Benchmarking Recommendation for Rigorous EvaluationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.3231891(1-20)Online publication date: 2022
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Djenouri YBelhadi ASrivastava GLin J(2022)Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data AnalysisIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.31698389:6(1748-1757)Online publication date: Dec-2022
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Alsini AHuynh DDatta A(2021)Hashtag Recommendation Methods for Twitter and Sina Weibo: A ReviewFuture Internet10.3390/fi1305012913:5(129)Online publication date: 14-May-2021
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Huang ZSun YWang WZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)Coupled Graph ODE for Learning Interacting System DynamicsProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467385(705-715)Online publication date: 14-Aug-2021
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