skip to main content
10.1145/3340531.3411933acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article

A Topic and Concept Integrated Model for Thread Recommendation in Online Health Communities

Published: 19 October 2020 Publication History

Abstract

Online health communities (OHCs) provide a popular channel for users to seek information, suggestions and support during their medical treatment and recovery processes. To help users find relevant information easily, we present CLIR, an effective system for recommending relevant discussion threads to users in OHCs. We identify that thread content and user interests can be categorized in two dimensions: topics and concepts. CLIR leverages Latent Dirichlet Allocation model to summarize the topic dimension and uses Convolutional Neural Network to encode the concept dimension. It then builds a thread neural network to capture thread characteristics and builds a user neural network to capture user interests by integrating these two dimensions and their interactions. Finally, it matches the target thread's characteristics with candidate users' interests to make recommendations. Experimental evaluation with multiple OHC datasets demonstrates the performance advantage of CLIR over the state-of-the-art recommender systems on various evaluation metrics.

Supplementary Material

MP4 File (3340531.3411933.mp4)
Presentation Video.

References

[1]
Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16). 265--283.
[2]
Deepak Agarwal and Bee-Chung Chen. 2010. fLDA: matrix factorization through latent dirichlet allocation. In Proceedings of the third ACM international conference on Web search and data mining. 91--100.
[3]
Trapit Bansal, David Belanger, and Andrew McCallum. 2016. Ask the gru: Multitask learning for deep text recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 107--114.
[4]
David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent Dirichlet allocation. Journal of machine Learning research 3, Jan (2003), 993--1022.
[5]
Honglong Chen, Jinnan Fu,Lei Zhang, Shuai Wang, Kai Lin, Leyi Shi, and Lianhai Wang. 2019. Deformable Convolutional Matrix Factorization for Document Context-Aware Recommendation in Social Networks. IEEE Access 7 (2019), 66347--66357.
[6]
Xu Chen, Mingyuan Zhou, and Lawrence Carin. 2012. The contextual focused topic model. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. 96--104.
[7]
Jin Yao Chin, Kaiqi Zhao, Shafiq Joty, and Gao Cong. 2018. ANR: Aspect-based neural recommender. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 147--156.
[8]
Hanjun Dai, Yichen Wang, Rakshit Trivedi, and Le Song. 2016. Recurrent co-evolutionary latent feature processes for continuous-time recommendation. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 29--34.
[9]
Judith E. Dayhoff. 1990. Neural Network Architectures: An Introduction. Van Nostrand Reinhold Co., New York, NY, USA.
[10]
Aminu Da'u and Naomie Salim. 2019. Sentiment-Aware deep recommender system with neural attention networks. IEEE Access 7 (2019), 45472--45484.
[11]
Ali Mamdouh Elkahky, Yang Song, and Xiaodong He. 2015. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 278--288.
[12]
Gunther Eysenbach. 2005. The law of attrition. Journal of medical Internet research 7, 1 (2005), e11.
[13]
Gottlob Frege. 1948. Sense and reference. The philosophical review 57, 3 (1948), 209--230.
[14]
Kostadin Georgiev and Preslav Nakov. 2013. A non-iid framework for collaborative filtering with restricted boltzmann machines. In International conference on machine learning. 1148--1156.
[15]
Yuyun Gong and Qi Zhang. 2016. Hashtag Recommendation Using Attention-Based Convolutional Neural Network. In IJCAI. 2782--2788.
[16]
Kishaloy Halder, Min-Yen Kan, and Kazunari Sugiyama. 2017. Health Forum Thread Recommendation Using an Interest Aware Topic Model. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 1589--1598.
[17]
Kishaloy Halder, Lahari Poddar, and Min-Yen Kan. 2018. Cold start thread recommendation as extreme multi-label classification. In Companion Proceedings of the The Web Conference 2018. 1911--1918.
[18]
Patrick Hansen, Richard Junior Bustamante, Tsung-Yen Yang, Elizabeth Tenorio, Christopher Brinton, Mung Chiang, and Andrew Lan. 2019. Predicting the Timing and Quality of Responses in Online Discussion Forums. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE, 1931--1940.
[19]
Wenyi Huang, Zhaohui Wu, Liang Chen, Prasenjit Mitra, and C Lee Giles. 2015. A Neural Probabilistic Model for Context Based Citation Recommendation. In AAAI. 2404--2410.
[20]
Kalervo Ja?rvelin and Jaana Keka?la?inen. 2002. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS) 20, 4 (2002), 422--446.
[21]
Ling Jiang and Christopher C Yang. 2016. Personalized Recommendation in Online Health Communities with Heterogeneous Network Mining. In Healthcare Informatics (ICHI), 2016 IEEE International Conference on. IEEE, 281--284.
[22]
A Kardan, Amir Narimani, and Foozhan Ataiefard. 2017. A Hybrid Approach for Thread Recommendation in MOOC Forums. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering 11, 10 (2017), 2195--2201.
[23]
Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, and Hwanjo Yu. 2016. Convolutional matrix factorization for document context-aware recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 233--240.
[24]
Donghyun Kim, Chanyoung Park, Jinoh Oh, and Hwanjo Yu. 2017. Deep hybrid recommender systems via exploiting document context and statistics of items. Information Sciences 417 (2017), 72--87.
[25]
Andrew S Lan, Jonathan C Spencer, Ziqi Chen, Christopher G Brinton, and Mung Chiang. 2018. Personalized Thread Recommendation for MOOC Discussion Forums. arXiv preprint arXiv:1806.08468 (2018).
[26]
Yann LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278-- 2324.
[27]
Mingda Li, Jinhe Shi, and Yi Chen. 2019. Analyzing Patient Decision Making in Online Health Communities. In 2019 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 1--8.
[28]
Xiaopeng Li and James She. 2017. Collaborative variational autoencoder for recommender systems. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. 305--314.
[29]
Jakub Macina, Ivan Srba, Joseph Jay Williams, and Maria Bielikova. 2017. Educational question routing in online student communities. In Proceedings of the Eleventh ACM Conference on Recommender Systems. 47--55.
[30]
Racheli Magnezi, Dafna Grosberg, Ilya Novikov, Arnona Ziv, Mordechai Shani, and Laurence S Freedman. 2015. Characteristics of patients seeking health information online via social health networks versus general Internet sites: a comparative study. Informatics for Health and Social Care 40, 2 (2015), 125--138.
[31]
Jon D Mcauliffe and David M Blei. 2008. Supervised topic models. In Advances in neural information processing systems. 121--128.
[32]
Andrew Kachites McCallum. 2002. Mallet: A machine learning for language toolkit. (2002).
[33]
Fei Mi and Boi Faltings. 2017. Adaptive Sequential Recommendation for Discussion Forums on MOOCs using Context Trees. In Proc. Intl. Conf. Educ. Data Min. 24--31.
[34]
Dongmin Hyun1 Chanyoung Park Min-Chul, Yang2 Ilhyeon Song2 Jung-Tae Lee, and Hwanjo Yu. 2018. Review Sentiment--Guided Scalable Deep Recommender System. (2018).
[35]
Tareq Nasralah, Cherie Bakker Noteboom, Abdullah Wahbeh, and Mohammad Aref Al-Ramahi. 2017. Online Health Recommendation System: A Social Support Perspective. (2017).
[36]
Hanh TH Nguyen, Martin Wistuba, Josif Grabocka, Lucas Rego Drumond, and Lars Schmidt-Thieme. 2017. Personalized Deep Learning for Tag Recommendation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 186--197.
[37]
Kenneth Olmstead Paul Hitlin. 2018. The Science People See on Social Media. https://www.pewresearch.org/science/2018/03/21/the-science-people-see-on- social- media/
[38]
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 1532--1543.
[39]
Daniel Ramage, David Hall, Ramesh Nallapati, and Christopher D Manning. 2009. Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1. Association for Computational Linguistics, 248--256.
[40]
Mark Steyvers and Tom Griffiths. 2007. Probabilistic topic models. Handbook of latent semantic analysis 427, 7 (2007), 424--440.
[41]
Rohan Tondulkar, Manisha Dubey, and Maunendra Sankar Desarkar. 2018. Get me the best: predicting best answerers in community question answering sites. In Proceedings of the 12th ACM Conference on Recommender Systems. 251--259.
[42]
Bartlomiej Twardowski. 2016. Modelling contextual information in session-aware recommender systems with neural networks. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 273--276.
[43]
Chong Wang and David M Blei. 2011. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 448--456.
[44]
Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. 2017. Irgan: A minimax game for unifying generative and discriminative information retrieval models. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 515--524.
[45]
Xi Wang, Kang Zhao, and Nick Street. 2014. Social support and user engagement in online health communities. In International Conference on Smart Health. Springer, 97--110.
[46]
Y.-C. Wang, R. Kraut, and J. M. Levine. 2012. To stay or leave? The relationship of emotional and informational support to commitment in online heath support groups. In ACM 2012 Conference on Computer Supported Cooperative Work (CSCW).
[47]
Zhibo Wang, Yongquan Zhang, Honglong Chen, Zhetao Li, and Feng Xia. 2018. Deep user modeling for content-based event recommendation in event-based social networks. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE, 1304--1312.
[48]
Minghua Xu and Shenghao Liu. 2019. Semantic-enhanced and context-aware hybrid collaborative filtering for event recommendation in event-based social networks. IEEE Access 7 (2019), 17493--17502.
[49]
Yin Zheng, Bangsheng Tang, Wenkui Ding, and Hanning Zhou. 2016. A Neural Autoregressive Approach to Collaborative Filtering. In International Conference on Machine Learning. 764--773.
[50]
Kathryn Zickuhr. 2013. Who's not online and why. https://www.pewresearch.org/internet/2013/09/25/whos- not- online- and- why- 2/

Cited By

View all
  • (2024)Efficient Health Class Recommendations for Kaiser Permanente Members: A Scalable Embedding-Based Approach2024 2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)10.1109/AIBThings63359.2024.10863384(1-5)Online publication date: 7-Sep-2024
  • (2024)Dynamic recommender system for chronic disease-focused online health communityExpert Systems with Applications10.1016/j.eswa.2024.125086258(125086)Online publication date: Dec-2024
  • (2024)Deep Learning-Based Recommendation Systems: Review and Critical AnalysisProceedings of Data Analytics and Management10.1007/978-981-99-6544-1_4(39-55)Online publication date: 14-Jan-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
October 2020
3619 pages
ISBN:9781450368599
DOI:10.1145/3340531
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 the author(s) 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].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 October 2020

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. discussion forum
  2. latent dirichlet allocation
  3. neural network
  4. online health community
  5. recommender systems
  6. thread recommendation

Qualifiers

  • Research-article

Funding Sources

  • The National Institutes of Health
  • Google Research
  • The Leir Foundation

Conference

CIKM '20
Sponsor:

Acceptance Rates

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

Upcoming Conference

CIKM '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)23
  • Downloads (Last 6 weeks)2
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Efficient Health Class Recommendations for Kaiser Permanente Members: A Scalable Embedding-Based Approach2024 2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)10.1109/AIBThings63359.2024.10863384(1-5)Online publication date: 7-Sep-2024
  • (2024)Dynamic recommender system for chronic disease-focused online health communityExpert Systems with Applications10.1016/j.eswa.2024.125086258(125086)Online publication date: Dec-2024
  • (2024)Deep Learning-Based Recommendation Systems: Review and Critical AnalysisProceedings of Data Analytics and Management10.1007/978-981-99-6544-1_4(39-55)Online publication date: 14-Jan-2024
  • (2023)Automatic Recommendation of Forum Threads and Reinforcement Activities in a Data Structure and Programming CourseApplied System Innovation10.3390/asi60500836:5(83)Online publication date: 21-Sep-2023
  • (2023)A Qualitative Study of the Latter Effects of the COVID-19 Pandemic on Patients Living With Chronic PainJournal of Patient Experience10.1177/2374373523119967310Online publication date: 13-Sep-2023
  • (2022)Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping ReviewInternational Journal of Environmental Research and Public Health10.3390/ijerph19221511519:22(15115)Online publication date: 16-Nov-2022
  • (2022)KETCHProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532008(492-501)Online publication date: 6-Jul-2022

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media