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Long-tail Hashtag Recommendation for Micro-videos with Graph Convolutional Network

Authors:

Liqiang NieAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 509 - 518

https://doi.org/10.1145/3357384.3357912

Published: 03 November 2019 Publication History

Abstract

Hashtags, a user provides to a micro-video, are the ones which can well describe the semantics of the micro-video's content in his/her mind. At the same time, hashtags have been widely used to facilitate various micro-video retrieval scenarios (e.g., search, browse, and categorization). Despite their importance, numerous micro-videos lack hashtags or contain inaccurate or incomplete hashtags. In light of this, hashtag recommendation, which suggests a list of hashtags to a user when he/she wants to annotate a post, becomes a crucial research problem. However, little attention has been paid to micro-video hashtag recommendation, mainly due to the following three reasons: 1) lack of benchmark dataset; 2) the temporal and multi-modality characteristics of micro-videos; and 3) hashtag sparsity and long-tail distributions. In this paper, we recommend hashtags for micro-videos by presenting a novel multi-view representation interactive embedding model with graph-based information propagation. It is capable of boosting the performance of micro-videos hashtag recommendation by jointly considering the sequential feature learning, the video-user-hashtag interaction, and the hashtag correlations. Extensive experiments on a constructed dataset demonstrate our proposed method outperforms state-of-the-art baselines. As a side research contribution, we have released our dataset and codes to facilitate the research in this community.

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Cited By

Che SLiu HLiu S(2024)Tagging Items with Emerging Tags: A Neural Topic Model Based Few-Shot Learning ApproachACM Transactions on Information Systems10.1145/364185942:4(1-37)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1145/3641859
Wang JKaratzoglou AArapakis IXin XGe XJose JSerra ESpezzano F(2024)Sparks of Surprise: Multi-objective Recommendations with Hierarchical Decision Transformers for Diversity, Novelty, and SerendipityProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679533(2358-2368)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679533
Li XLiang SHou YMa T(2024)StratMed: Relevance stratification between biomedical entities for sparsity on medication recommendationKnowledge-Based Systems10.1016/j.knosys.2023.111239284(111239)Online publication date: Jan-2024
https://doi.org/10.1016/j.knosys.2023.111239
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Index Terms

Long-tail Hashtag Recommendation for Micro-videos with Graph Convolutional Network
1. Information systems
  1. World Wide Web
    1. Web searching and information discovery

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Published In

cover image ACM Conferences

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 November 2019

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National Natural Science Foundation of China
Shandong Provincial Natural Science and Foundation
Future Talents Research Funds of Shandong University
the Project of Thousand Youth Talents 2016

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CIKM '19

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CIKM '19: The 28th ACM International Conference on Information and Knowledge Management

November 3 - 7, 2019

Beijing, China

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Che SLiu HLiu S(2024)Tagging Items with Emerging Tags: A Neural Topic Model Based Few-Shot Learning ApproachACM Transactions on Information Systems10.1145/364185942:4(1-37)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1145/3641859
Wang JKaratzoglou AArapakis IXin XGe XJose JSerra ESpezzano F(2024)Sparks of Surprise: Multi-objective Recommendations with Hierarchical Decision Transformers for Diversity, Novelty, and SerendipityProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679533(2358-2368)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679533
Li XLiang SHou YMa T(2024)StratMed: Relevance stratification between biomedical entities for sparsity on medication recommendationKnowledge-Based Systems10.1016/j.knosys.2023.111239284(111239)Online publication date: Jan-2024
https://doi.org/10.1016/j.knosys.2023.111239
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
Bansal SGowda KRehman MRaghaw CKumar N(2024)A hybrid filtering for micro-video hashtag recommendation using graph-based deep neural networkEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109417138(109417)Online publication date: Dec-2024
https://doi.org/10.1016/j.engappai.2024.109417
Fu ZNiu XMaher M(2023)Deep Learning Models for Serendipity Recommendations: A Survey and New PerspectivesACM Computing Surveys10.1145/360514556:1(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3605145
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
Maćkowski MBrzoza PKawulok MMeisel RSpinczyk D(2023)Multimodal Presentation of Interactive Audio-Tactile Graphics Supporting the Perception of Visual Information by Blind PeopleACM Transactions on Multimedia Computing, Communications, and Applications10.1145/358607619:5s(1-22)Online publication date: 7-Jun-2023
https://dl.acm.org/doi/10.1145/3586076
Ricci SUricchio TDel Bimbo A(2023)Meta-learning Advisor Networks for Long-tail and Noisy Labels in Social Image ClassificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/358436019:5s(1-23)Online publication date: 7-Jun-2023
https://dl.acm.org/doi/10.1145/3584360
Liu HYang XXu C(2023)Counterfactual Scenario-relevant Knowledge-enriched Multi-modal Emotion ReasoningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/358369019:5s(1-25)Online publication date: 7-Jun-2023
https://dl.acm.org/doi/10.1145/3583690
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