research-article

Tag2Word: Using Tags to Generate Words for Content Based Tag Recommendation

Authors:

Jian LuAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 2287 - 2292

https://doi.org/10.1145/2983323.2983682

Published: 24 October 2016 Publication History

Abstract

Tag recommendation is helpful for the categorization and searching of online content. Existing tag recommendation methods can be divided into collaborative filtering methods and content based methods. In this paper, we put our focus on the content based tag recommendation due to its wider applicability. Our key observation is the tag-content co-occurrence, i.e., many tags have appeared multiple times in the corresponding content. Based on this observation, we propose a generative model (Tag2Word), where we generate the words based on the tag-word distribution as well as the tag itself. Experimental evaluations on real data sets demonstrate that the proposed method outperforms several existing methods in terms of recommendation accuracy, while enjoying linear scalability.

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Cited By

Santos PPato MDatia NSobral JLeitão NRamos Ferreira MGomes N(2023)Recommending Words Using a Bayesian NetworkElectronics10.3390/electronics1210221812:10(2218)Online publication date: 12-May-2023
https://doi.org/10.3390/electronics12102218
Shokrzadeh ZFeizi-Derakhshi MBalafar MBagherzadeh Mohasefi J(2023)Graph-Based Recommendation System Enhanced by Community DetectionScientific Programming10.1155/2023/50737692023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/5073769
Parizotto RCoelho BNunes DHaque ISchaeffer-Filho A(2023)Offloading Machine Learning to Programmable Data Planes: A Systematic SurveyACM Computing Surveys10.1145/360515356:1(1-34)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3605153
Show More Cited By

Index Terms

Tag2Word: Using Tags to Generate Words for Content Based Tag Recommendation
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

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Published: 24 October 2016

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October 24 - 28, 2016

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CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

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

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Cited By

Santos PPato MDatia NSobral JLeitão NRamos Ferreira MGomes N(2023)Recommending Words Using a Bayesian NetworkElectronics10.3390/electronics1210221812:10(2218)Online publication date: 12-May-2023
https://doi.org/10.3390/electronics12102218
Shokrzadeh ZFeizi-Derakhshi MBalafar MBagherzadeh Mohasefi J(2023)Graph-Based Recommendation System Enhanced by Community DetectionScientific Programming10.1155/2023/50737692023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/5073769
Parizotto RCoelho BNunes DHaque ISchaeffer-Filho A(2023)Offloading Machine Learning to Programmable Data Planes: A Systematic SurveyACM Computing Surveys10.1145/360515356:1(1-34)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3605153
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
Liu YLi QWang BZhang YSong D(2023)A Survey of Quantum-cognitively Inspired Sentiment Analysis ModelsACM Computing Surveys10.1145/360455056:1(1-37)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3604550
Zolfaghari SSuravee SRiboni DYordanova K(2023)Sensor-Based Locomotion Data Mining for Supporting the Diagnosis of Neurodegenerative Disorders: A SurveyACM Computing Surveys10.1145/360349556:1(1-36)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3603495
Xu PXia MXiao LLiu HLiu BJing LYu J(2023)Textual tag recommendation with multi-tag topical attentionNeurocomputing10.1016/j.neucom.2023.03.051537(73-84)Online publication date: Jun-2023
https://doi.org/10.1016/j.neucom.2023.03.051
Ashvanth RDeepak GSheeba Priyadarshini JSanthanavijayan A(2023)KMetaTagger: A Knowledge Centric Metadata Driven Hybrid Tag Recommendation Model Encompassing Machine IntelligenceIntelligent Systems Design and Applications10.1007/978-3-031-27440-4_1(1-11)Online publication date: 31-May-2023
https://doi.org/10.1007/978-3-031-27440-4_1
Chen YLai KLiu DChen M(2022)TAGNet: Triplet-Attention Graph Networks for Hashtag RecommendationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2021.307459932:3(1148-1159)Online publication date: Mar-2022
https://doi.org/10.1109/TCSVT.2021.3074599
Zhao WZhang AShang LYu YZhang LWang CChen JYin H(2022)Hyperbolic Personalized Tag RecommendationDatabase Systems for Advanced Applications10.1007/978-3-031-00126-0_14(216-231)Online publication date: 8-Apr-2022
https://doi.org/10.1007/978-3-031-00126-0_14
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