research-article

Health Forum Thread Recommendation Using an Interest Aware Topic Model

Authors:

Kishaloy Halder,

Kazunari SugiyamaAuthors Info & Claims

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Pages 1589 - 1598

https://doi.org/10.1145/3132847.3132946

Published: 06 November 2017 Publication History

Abstract

We introduce a general, interest-aware topic model (IATM), in which known higher-level interests on topics expressed by each user can be modeled. We then specialize the IATM for use in consumer health forum thread recommendation by equating each user's self-reported medical conditions as interests and topics as symptoms of treatments for recommendation. The IATM additionally models the implicit interests embodied by users' textual descriptions in their profiles. To further enhance the personalized nature of the recommendations, we introduce jointly normalized collaborative topic regression (JNCTR) which captures how users interact with the various symptoms belonging to the same clinical condition.

In our experiments on two real-world consumer health forums, our proposed model significantly outperforms competitive state-of-the-art baselines by over 10% in recall. Importantly, we show that our IATM+JNCTR pipeline also imbues the recommendation process with added transparency, allowing a recommendation system to justify its recommendation with respect to each user's interest in certain health conditions.

References

[1]

D. Agarwal and B.-C. Chen. fLDA: Matrix Factorization through Latent Dirichlet Allocation. In Proc. of WSDM'10, pages 91--100, 2010.

Digital Library

[2]

T. Bansal, M. Das, and C. Bhattacharyya. Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles. In Proc. of RecSys'15, pages 195--202, 2015.

Digital Library

[3]

A. Batenburg and E. Das. Emotional Coping Differences Among Breast cancer Patients from an Online Support Group: A Cross-Sectional Study. Journal of Medical Internet Research (JMIR), 16(2):e28, 2014.

[4]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research (JMLR), 3:993--1022, 2003.

Digital Library

[5]

L. Charlin, R. S. Zemel, and H. Larochelle. Leveraging User Libraries to Bootstrap Collaborative Filtering. In Proc. of KDD'14, pages 173--182, 2014.

Digital Library

[6]

X. Chen, M. Zhou, and L. Carin. The Contextual Focused Topic Model. In Proc. of KDD '12, pages 96--104, 2012.

Digital Library

[7]

K. Halder, L. Poddar, and M.-Y. Kan. Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach. In Proc. of WASSA '17, pages 127--135, 2017.

[8]

X. He, T. Chen, M.-Y. Kan, and X. Chen. TriRank: Review-aware Explainable Recommendation by Modeling Aspects. In Proc. of CIKM'15, pages 1661--1670, 2015.

Digital Library

[9]

Z. He, M. S. Park, and Z. Chen. UMLS-Based Analysis of Medical Terminology Coverage for Tags in Diabetes-Related Blogs. IConference 2016 Proceedings, 2016.

[10]

S. Kanthawala, A. Vermeesch, B. Given, and J. Huh. Answers to Health Questions: Internet Search Results Versus Online Health Community Responses. Journal of Medical Internet Research (JMIR), 18(4):e95, 2016.

[11]

A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver. Multiverse Recommendation: N-dimensional Tensor Factorization for Context-aware Collaborative Filtering. In Proc. of RecSys'10, pages 79--86, 2010.

Digital Library

[12]

S. Kim. An Exploratory Study of User-centered Indexing of Published Biomedical Images. Journal of the Medical Library Association (JMLA), 101(1):73--76, 2013.

[13]

D. D. Lee and H. S. Seung. Algorithms for Non-negative Matrix Factorization. In Proc. of NIPS'01, pages 556--562, 2001.

Digital Library

[14]

J. Li, Y.-L. Theng, and S. Foo. Predictors of Online Health Information Seeking Behavior: Changes between 2002 and 2012. Health Informatics Journal, 22(4):804--814, 2016.

[15]

J. Lin, K. Sugiyama, M.-Y. Kan, and T.-S. Chua. Addressing Cold-Start in App Recommendation: Latent User Models Constructed from Twitter Followers. In Proc. of SIGIR'13, pages 283--292, 2013.

Digital Library

[16]

R. Magnezi, D. Grosberg, I. Novikov, A. Ziv, M. Shani, and L. S. Freedman. 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):125--138, 2015.

[17]

J. D. Mcauliffe and D. M. Blei. Supervised Topic Models. In Proc. of NIPS'07, pages 121--128. 2008.

Digital Library

[18]

A. Meier, J. E. Lyons, G. Frydman, M. Forlenza, and K. B. Rimer. How Cancer Survivors Provide Support on Cancer-Related Internet Mailing Lists. Journal of Medical Internet Research (JMIR), 9(2):e12, 2007.

[19]

T. V. Nguyen, A. Karatzoglou, and L. Baltrunas. Gaussian Process Factorization Machines for Context-aware Recommendations. In Proc. of SIGIR'14, pages 63--72, 2014.

Digital Library

[20]

L. Nie, Y.-L. Zhao, M. Akbari, J. Shen, and T.-S. Chua. Bridging the Vocabulary Gap between Health Seekers and Healthcare Knowledge. IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(2):396--409, 2015.

[21]

H. J. Oh, C. Lauckner, J. Boehmer, R. Fewins-Bliss, and K. Li. Facebooking for Health: An Examination into the Solicitation and Effects of Health-Related Social Support on Social Networking Sites. Computers in Human Behavior, 29(5):2072--2080, 2013.

[22]

A. Omari, D. Carmel, O. Rokhlenko, and I. Szpektor. Novelty based Ranking of Human Answers for Community Questions. In Proc. of SIGIR'16, pages 215--224, 2016.

Digital Library

[23]

J. Palotti, L. Goeuriot, G. Zuccon, and A. Hanbury. Ranking Health Web Pages with Relevance and Understandability. In Proc. of SIGIR'16, pages 965--968, 2016.

Digital Library

[24]

J. S. Pedro and A. Karatzoglou. Question Recommendation for Collaborative Question Answering Systems with RankSLDA. In Proc. of RecSys'14, pages 193--200, 2014.

Digital Library

[25]

L. Poddar, W. Hsu, and M. L. Lee. Author-aware Aspect Topic Sentiment Model to Retrieve Supporting Opinions from Reviews. In Proc. of EMNLP '17, pages 483--492, 2017.

[26]

D. Ramage, D. Hall, R. Nallapati, and C. D. Manning. Labeled LDA: A Supervised Topic Model for Credit Attribution in Multi-labeled Corpora. In Proc. of EMNLP'09, pages 248--256, 2009.

Digital Library

[27]

S. Rendle. Factorization Machines with libFM. ACM Transactions on Intelligent Systems and Technology (TIST), 3(3):Article No. 57, 2012.

Digital Library

[28]

M. Rosen-Zvi, T. Griffiths, M. Steyvers, and P. Smyth. The Author-Topic Model for Authors and Documents. In Proc. of UAI'04, pages 487--494, 2004.

Digital Library

[29]

L. Schnitzler, S. K. Smith, H. L. Shepherd, J. Shaw, S. Dong, D. M. Carpenter, F. Nguyen, and H. M. Dhillon. Communication During Radiation Therapy Education Sessions: The Role of Medical Jargon and Emotional Support in Clarifying Patient Confusion. Patient Education and Counseling, 100(1):112--120, 2017.

[30]

E. Shmueli, A. Kagian, Y. Koren, and R. Lempel. Care to Comment?: Recommendations for Commenting on News Stories. In Proc. of WWW 2012, pages 429--438, 2012.

Digital Library

[31]

M. D. Smucker, D. Kulp, and J. Allan. Dirichlet Mixtures for Query Estimation in Information Retrieval. Technical Report IR-445, Center for Intelligent Information Retrieval, University of Massachusetts, Amherst, 2005.

[32]

C. Wang and D. M. Blei. Collaborative Topic Modeling for Recommending Scientific Articles. In Proc. of KDD'11, pages 448--456, 2011.

Digital Library

[33]

J. Wang, X. Hu, Z. Li, W. Chao, and B. Hu. Learning to Recommend Questions Based on Public Interest. In Proc. of CIKM'11, pages 2029--2032, 2011.

Digital Library

[34]

Y.-C. Wang, R. Kraut, and J. M. Levine. To Stay or Leave?: The Relationship of Emotional and Informational Support to Commitment in Online Health Support Groups. In Proc. of CSCW '12, pages 833--842, 2012.

Digital Library

[35]

S. Williamson, C. Wang, K. A. Heller, and D. M. Blei. The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling. In Proc. of ICML '10, pages 1151--1158, 2010.

Digital Library

[36]

B. Xu, J. Bu, C. Chen, and D. Cai. An Exploration of Improving Collaborative Recommender Systems via User-Item Subgroups. In Proc. of WWW'12, pages 21--30, 2012.

Digital Library

[37]

M. Zhang, J. Tang, X. Zhang, and X. Xue. Addressing Cold Start in Recommender Systems: A Semi-supervised Co-training Algorithm. In Proc. of SIGIR'14, pages 73--82, 2014.

Digital Library

[38]

Y. Zhang, G. Lai, M. Zhang, Y. Zhang, Y. Liu, and S. Ma. Explicit Factor Models for Explainable Recommendation based on Phrase-level Sentiment Analysis. In Proc. of SIGIR'14, pages 83--92, 2014.

Digital Library

[39]

T. C. Zhou, M. R.-T. Lyu, I. King, and J. Lou. Learning to Suggest Questions in Social Media. Knowledge and Information Systems (KAIS), 43(2):389--416, 2015.

Digital Library

Cited By

Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Sun YZhou JJi MPei LWang Z(2023)Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence MappingJournal of Medical Internet Research10.2196/3818425(e38184)Online publication date: 19-Jan-2023
https://doi.org/10.2196/38184
Cai YYu FKumar MGladney RMostafa J(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
https://doi.org/10.3390/ijerph192215115
Show More Cited By

Recommendations

A framework for diversifying recommendation lists by user interest expansion

Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N ...
Hybrid Trust-Aware Model for Personalized Top-N Recommendation
CODS '17: Proceedings of the 4th ACM IKDD Conferences on Data Sciences

Due to the large quantity and diversity of content being easily available to users, recommender systems (RS) have become an integral part of nearly every online system. They allow users to resolve the information overload problem by proactively ...
Improving recommendation lists through topic diversification
WWW '05: Proceedings of the 14th international conference on World Wide Web

In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

November 2017

2604 pages

ISBN:9781450349185

DOI:10.1145/3132847

General Chairs:
Ee-Peng Lim
Singapore Management University, Singapore
,
Marianne Winslett
University of Illinois at Urbana-Champaign, USA, and Advanced Digital Sciences Center, Singapore
,
Program Chairs:
Mark Sanderson
RMIT, Australia
,
Ada Fu
Chinese University of Hong Kong, Hong Kong
,
Jimeng Sun
Georgia Tech, USA
,
Shane Culpepper
RMIT, Australia
,
Eric Lo
Chinese University of Hong Kong, Hong Kong
,
Joyce Ho
Emory University, USA
,
Debora Donato
Mix Tech, Inc., USA
,
Rakesh Agrawal
Data Insights Laboratories, USA
,
Yu Zheng
Microsoft Research Asia, China
,
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Aixin Sun
Nanyang Technological University, Singapore
,
Vincent S. Tseng
National Cheng Kung University, Taiwan
,
Chenliang Li
Wuhan University, China

Copyright © 2017 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CIKM '17

Sponsor:

CIKM '17: ACM Conference on Information and Knowledge Management

November 6 - 10, 2017

Singapore, Singapore

Acceptance Rates

CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;

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

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
313
Total Downloads

Downloads (Last 12 months)17
Downloads (Last 6 weeks)1

Reflects downloads up to 21 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Sun YZhou JJi MPei LWang Z(2023)Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence MappingJournal of Medical Internet Research10.2196/3818425(e38184)Online publication date: 19-Jan-2023
https://doi.org/10.2196/38184
Cai YYu FKumar MGladney RMostafa J(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
https://doi.org/10.3390/ijerph192215115
Thaker KChi YBirkhoff SHe DDonovan HRosenblum LBrusilovsky PHui VLee Y(2022)Exploring Resource-Sharing Behaviors for Finding Relevant Health Resources: Analysis of an Online Ovarian Cancer CommunityJMIR Cancer10.2196/331108:2(e33110)Online publication date: 12-Apr-2022
https://doi.org/10.2196/33110
Cui LLee DAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(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
https://dl.acm.org/doi/10.1145/3477495.3532008
De Croon RVan Houdt LHtun NŠtiglic GVanden Abeele VVerbert K(2021)Health Recommender Systems: Systematic ReviewJournal of Medical Internet Research10.2196/1803523:6(e18035)Online publication date: 29-Jun-2021
https://doi.org/10.2196/18035
Sun WLiu XShen F(2021)Learning Dynamic User Interactions for Online Forum Commenting Prediction2021 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM51629.2021.00168(1342-1347)Online publication date: Dec-2021
https://doi.org/10.1109/ICDM51629.2021.00168
Li MGao WChen Yd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)A Topic and Concept Integrated Model for Thread Recommendation in Online Health CommunitiesProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3411933(765-774)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3411933
Wan HYu QLiu KGao XDing JLiu YChen WWang X(2018)Recommend related discussion forum posts to students in the small private online courseProceedings of ACM Turing Celebration Conference - China10.1145/3210713.3210747(134-135)Online publication date: 18-May-2018
https://dl.acm.org/doi/10.1145/3210713.3210747
Halder KPoddar LKan MChampin PGandon FMédini LLalmas MIpeirotis P(2018)Cold Start Thread Recommendation as Extreme Multi-label ClassificationCompanion Proceedings of the The Web Conference 201810.1145/3184558.3191659(1911-1918)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3184558.3191659

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents