Journal Articles

Constructing Recommendation Systems for Effective Health Messages Using Content, Collaborative, and Hybrid Algorithms

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Theoretical and empirical approaches to the design of effective messages to increase healthy and reduce risky behavior have shown only incremental progress. This article explores approaches to the development of a “recommendation system” for archives of public health messages. Recommendation systems are algorithms operating on dense data involving both individual preferences and objective message features. Their goal is to predict ratings for items (i.e., messages) not previously seen by the user on content similarity, prior preference patterns, or their combination. Standard approaches to message testing and research, while making progress, suffer from very slow accumulation of knowledge. This article seeks to leapfrog conventional models of message research, taking advantage of modeling developments in recommendation systems from the commercial arena. After sketching key components in developing recommendation algorithms, this article concludes with reflections on the implications of these approaches in both theory development and application.

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Detail Information

Series Title

THE ANNALS OF THE AMERICAN ACADEMY OF POLITICAL AND SOCIAL SCIENCE

Call Number

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Publisher

Thousand Oaks : Sage Publications., 2015

Collation

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Language

English

ISBN/ISSN

978-1-5063-1463-1

Classification

NONE

Content Type

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Media Type

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Carrier Type

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Edition

Volume 659, May 2015, pages 290-306

Subject(s)

Recommendation algorithms
Content-based approaches
Collaborative filtering
Hybrid approaches

Specific Detail Info

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Statement of Responsibility

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