research-article

User Personalized Satisfaction Prediction via Multiple Instance Deep Learning

Authors:

Deng CaiAuthors Info & Claims

WWW '17: Proceedings of the 26th International Conference on World Wide Web

Pages 907 - 915

https://doi.org/10.1145/3038912.3052599

Published: 03 April 2017 Publication History

Abstract

Community question answering(CQA) services have arisen as a popular knowledge sharing pattern for netizens. With abundant interactions among users, individuals are capable of obtaining satisfactory information. However, it is not effective for users to attain satisfying answers within minutes. Users have to check the progress over time until the appropriate answers submitted. We address this problem as a user personalized satisfaction prediction task. Existing methods usually exploit manual feature selection. It is not desirable as it requires careful design and is labor intensive. In this paper, we settle this issue by developing a new multiple instance deep learning framework. Specifically, in our settings, each question follows a multiple instance learning assumption, where its obtained answers can be regarded as instance sets in a bag and we define the question resolved with at least one satisfactory answer. We design an efficient framework exploiting multiple instance learning property with deep learning tactic to model the question-answer pairs relevance and rank the asker's satisfaction possibility. Extensive experiments on large-scale datasets from different forums of Stack Exchange demonstrate the feasibility of our proposed framework in predicting asker personalized satisfaction.

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Cited By

Ding RXie RHao XYang XGe KZhang XZhou JLin L(2023)Interpretable User Retention Modeling in RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608818(702-708)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608818
Choudhry NAbawajy JHuda SRao I(2023)A Comprehensive Survey of Machine Learning Methods for Surveillance Videos Anomaly DetectionIEEE Access10.1109/ACCESS.2023.332180011(114680-114713)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3321800
Yang RMa JLin HGao WAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance LearningProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531930(1761-1772)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531930
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Index Terms

User Personalized Satisfaction Prediction via Multiple Instance Deep Learning
1. Applied computing
  1. Document management and text processing
    1. Document searching
2. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '17: Proceedings of the 26th International Conference on World Wide Web

April 2017

1678 pages

ISBN:9781450349130

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Copyright © 2017 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

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Research-article

Funding Sources

National Basic Research Program of China (973 Program)
Fundamental Research Funds for the Central Universities
the Key Laboratory of Advanced Information Science and Network Technology of Beijing
National Natural Science Foundation of China

Conference

WWW '17

Sponsor:

IW3C2

WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

Acceptance Rates

WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Ding RXie RHao XYang XGe KZhang XZhou JLin L(2023)Interpretable User Retention Modeling in RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608818(702-708)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608818
Choudhry NAbawajy JHuda SRao I(2023)A Comprehensive Survey of Machine Learning Methods for Surveillance Videos Anomaly DetectionIEEE Access10.1109/ACCESS.2023.332180011(114680-114713)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3321800
Yang RMa JLin HGao WAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance LearningProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531930(1761-1772)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531930
Kurni MMrunalini MSaritha K(2021)Deep Learning Techniques for Social Media AnalyticsPrinciples of Social Networking10.1007/978-981-16-3398-0_18(413-442)Online publication date: 19-Aug-2021
https://doi.org/10.1007/978-981-16-3398-0_18
Han XJin R(2020)A Small Sample Image Recognition Method Based on ResNet and Transfer Learning2020 5th International Conference on Computational Intelligence and Applications (ICCIA)10.1109/ICCIA49625.2020.00022(76-81)Online publication date: Jul-2020
https://doi.org/10.1109/ICCIA49625.2020.00022
Liang S(2019)Unsupervised Semantic Generative Adversarial Networks for Expert RetrievalThe World Wide Web Conference10.1145/3308558.3313625(1039-1050)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313625
Hayat MDaud AAlshdadi ABanjar AAbbasi RBao YDawood H(2019)Towards Deep Learning Prospects: Insights for Social Media AnalyticsIEEE Access10.1109/ACCESS.2019.2905101(1-1)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2905101
Brasher RWagle JRoth N(2019)Sometimes You Want to Go Where Everybody Knows Your NameIntelligent Computing10.1007/978-3-030-22871-2_44(648-658)Online publication date: 23-Jun-2019
https://doi.org/10.1007/978-3-030-22871-2_44
Yamamoto AOsanai HNakao AYamamoto SYamaguchi SKamiyama TOguchi M(2019)Prediction of Traffic Congestion on Wired and Wireless Networks Using RNNProceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 201910.1007/978-3-030-19063-7_26(315-328)Online publication date: 23-May-2019
https://doi.org/10.1007/978-3-030-19063-7_26
Zhu YZhu JHou JLi YWang BGuan ZCai D(2018)A brand-level ranking system with the customized attention-GRU modelProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304222.3304318(3947-3953)Online publication date: 13-Jul-2018
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