skip to main content
10.1145/2645710.2645768acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
short-paper

PERSPeCT: collaborative filtering for tailored health communications

Published: 06 October 2014 Publication History

Abstract

The goal of computer tailored health communications (CTHC) is to elicit healthy behavior changes by sending motivational messages personalized to individual patients. One prominent weakness of many existing CTHC systems is that they are based on expert-written rules and thus have no ability to learn from their users over time. One solution to this problem is to develop CTHC systems based on the principles of collaborative filtering, but this approach has not been widely studied. In this paper, we present a case study evaluating nine rating prediction methods for use in the Patient Experience Recommender System for Persuasive Communication Tailoring, a system developed for use in a clinical trial of CTHC-based smoking cessation support interventions.

References

[1]
K. Ashing-Giwa. Health behavior change models and their socio-cultural relevance for breast cancer screening in African American women. Women Health, 28(4):53--71, 1999.
[2]
J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering, 1999.
[3]
M. Jahrer, A. Töscher, and R. Legenstein. Combining predictions for accurate recommender systems. In Proceedings of the 16th ACM SIGKDD, pages 693--702. ACM, 2010.
[4]
M. Kreuter. Tailoring health messages: customizing communication with computer technology. LEA's communication series. L. Erlbaum, Mahwah, N.J., 2000.
[5]
B. Marlin, R. Adams, R. Sadasivam, and T. Houston. Towards collaborative filtering recommender systems for tailored health communications. In Proceedings of the AMIA 2013 Annual Symposium, pages 1600--1607, 2013.
[6]
J. McAuley and J. Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. In Proceedings of the 7th ACM conference on Recommender systems, pages 165--172. ACM, 2013.
[7]
R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. pages 880--887. ACM, 2008.
[8]
R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. Advances in neural information processing systems, 20:1257--1264, 2008.
[9]
T. Segaran. Programming collective intelligence: building smart web 2.0 applications. O'Reilly, Beijing; Sebastapol CA, 1st edition, 2007.
[10]
A. P. Singh and G. J. Gordon. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD, pages 650--658. ACM, 2008.
[11]
A. P. Singh and G. J. Gordon. A bayesian matrix factorization model for relational data. In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, 2010.
[12]
V. J. Strecher, J. B. McClure, G. L. Alexander, B. Chakraborty, V. N. Nair, J. M. Konkel, S. M. Greene, L. M. Collins, C. C. Carlier, C. J. Wiese, R. J. Little, C. S. Pomerleau, and O. F. Pomerleau. Web-based smoking-cessation programs: results of a randomized trial. Am J Prev Med, 34(5):373--81, 2008.

Cited By

View all
  • (2023)Effect of a Machine Learning Recommender System and Viral Peer Marketing Intervention on Smoking CessationJAMA Network Open10.1001/jamanetworkopen.2022.506656:1(e2250665)Online publication date: 12-Jan-2023
  • (2021)Health Recommender Systems: Systematic ReviewJournal of Medical Internet Research10.2196/1803523:6(e18035)Online publication date: 29-Jun-2021
  • (2021)Improving information retrieval from electronic health records using dynamic and multi-collaborative filteringPLOS ONE10.1371/journal.pone.025546716:8(e0255467)Online publication date: 5-Aug-2021
  • Show More Cited By

Index Terms

  1. PERSPeCT: collaborative filtering for tailored health communications

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    RecSys '14: Proceedings of the 8th ACM Conference on Recommender systems
    October 2014
    458 pages
    ISBN:9781450326681
    DOI:10.1145/2645710
    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

    In-Cooperation

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 06 October 2014

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. recommender systems
    2. tailored health communications

    Qualifiers

    • Short-paper

    Funding Sources

    Conference

    RecSys'14
    Sponsor:
    RecSys'14: Eighth ACM Conference on Recommender Systems
    October 6 - 10, 2014
    California, Foster City, Silicon Valley, USA

    Acceptance Rates

    RecSys '14 Paper Acceptance Rate 35 of 234 submissions, 15%;
    Overall Acceptance Rate 254 of 1,295 submissions, 20%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)9
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 20 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2023)Effect of a Machine Learning Recommender System and Viral Peer Marketing Intervention on Smoking CessationJAMA Network Open10.1001/jamanetworkopen.2022.506656:1(e2250665)Online publication date: 12-Jan-2023
    • (2021)Health Recommender Systems: Systematic ReviewJournal of Medical Internet Research10.2196/1803523:6(e18035)Online publication date: 29-Jun-2021
    • (2021)Improving information retrieval from electronic health records using dynamic and multi-collaborative filteringPLOS ONE10.1371/journal.pone.025546716:8(e0255467)Online publication date: 5-Aug-2021
    • (2021)Evaluating the use of a recommender system for selecting optimal messages for smoking cessation: patterns and effects of user-system engagementBMC Public Health10.1186/s12889-021-11803-821:1Online publication date: 26-Sep-2021
    • (2021)Assisted education: Using predictive model to avoid school dropout in e-learning systemsIntelligent Systems and Learning Data Analytics in Online Education10.1016/B978-0-12-823410-5.00002-4(153-178)Online publication date: 2021
    • (2020)Impact of a Collective Intelligence Tailored Messaging System on Smoking Cessation between African American and White smokers: A Quasi-experimental Design (Preprint)JMIR mHealth and uHealth10.2196/18064Online publication date: 31-Jan-2020
    • (2020)Heath-PRIOR: An Intelligent Ensemble Architecture to Identify Risk Cases in HealthcareIEEE Access10.1109/ACCESS.2020.30423428(217150-217168)Online publication date: 2020
    • (2018)Systematic Literature Review on Empirical Results and Practical Implementations of Healthcare Recommender SystemsInternational Journal of Healthcare Information Systems and Informatics10.4018/IJHISI.201510010110:4(1-21)Online publication date: 16-Dec-2018
    • (2018)Systematic Literature Review on Empirical Results and Practical Implementations of Healthcare Recommender SystemsIntelligent Systems10.4018/978-1-5225-5643-5.ch098(2206-2226)Online publication date: 2018
    • (2018)Recommender Systems in HealthcareHandbook of Research on Emerging Perspectives on Healthcare Information Systems and Informatics10.4018/978-1-5225-5460-8.ch014(323-346)Online publication date: 11-May-2018
    • Show More Cited By

    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