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Constructing Recommender Systems for Effective Health Messages: Smoking Cessation

构建有效健康信息的推荐系统：戒烟

基本信息

批准号：
8704397
负责人：
Joseph Nicholas Cappella
金额：
$ 19.32万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-09-23 至 2016-07-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): Successful public health campaigns depend in large measure on how effectively information is communicated. Designing effective messages for the public's health is both an art and a science with the art dominating because knowledge generated by the science is accumulating too slowly and with insufficient theoretical guidance. The research proposed here abandons standard experimental approaches to message design and abandons theory development in favor of the development of a "recommendation machine" modeled after commercial systems. This approach will allow message recommendations tailored to individual preferences based on algorithms for content similarity, preference similarity or their combination. Recommendation systems are essentially derived algorithms operating on dense data involving both preferences for messages (ratings by smokers) and objective message features (content). Their goal is to predict a user's ratings for messages not previously seen by the user. Conventional approaches to message research advance the science of message design too slowly, are driven by inadequate theory, and require very complex factorial interactions among audience characteristics, message features and the target behavior. The development of a recommendation machine for health messages will operate on a large archive of messages, dense preference data from smokers, and extensive (and mostly automated) assessment of the objective features of messages. The results will provide a procedure for the selection of effective messages from a large archive that will be tailored to a specific target person. Unlike tailoring research, no a priori assumptions will be made about which audience characteristics would need to be identified to constrain message selection. Recommendation systems have the potential to transform research about effective messages. The outcomes would include (1) an algorithm for preferences for effective (smoking cessation) messages; (2) a leap beyond approaches to message design side-stepping the tedious work in one-feature-at-a-time experiments; (3) an approach employing methods familiar to anyone ever having bought a book on Amazon or selected a movie via Netflix; (4) setting the stage for automatic user friendly recommender systems. Message selection processes for behaviors to increase health and lower risk would change radically. Applications using new media such as mobile technologies and personalized health web sites would be enabled as well. The research proposed: (1) prepares existing data to use in pretesting recommendation systems; (2) develops recommendation algorithms that are hybrids of collaborative and content approaches using state-of- the-art procedures from the commercial arena; (3) tests hybrid algorithms in a sample of smokers comparing the preferences for recommended messages to two comparison conditions; (4) follows up to determine whether differences in smoking cessation intentions differ between those receiving messages suggested via the recommender algorithms vs. those receiving a random selection or a "most preferred" set.

成功的公共卫生运动在很大程度上取决于如何有效地传播信息。为公众健康设计有效的信息既是一门艺术，也是一门科学，艺术占主导地位，因为科学产生的知识积累太慢，理论指导不足。这里提出的研究放弃了标准的实验方法，以信息设计和放弃有利于商业系统后的“推荐机”的发展理论的发展。这种方法将允许基于内容相似性、偏好相似性或其组合的算法来针对个人偏好定制消息推荐。推荐系统本质上是基于密集数据的衍生算法，涉及对消息的偏好（吸烟者的评级）和客观消息特征（内容）。他们的目标是预测用户对用户以前没有看到的消息的评级。传统的信息研究方法对信息设计的科学进展太慢，理论不足，需要非常复杂的因素之间的相互作用的受众特征，信息特征和目标行为。健康信息推荐机器的开发将基于大量信息档案、吸烟者的密集偏好数据以及对信息客观特征的广泛（且大多是自动化的）评估。结果将提供一个程序，从一个大型档案中选择有效的信息，将适合特定的目标人。与剪裁研究不同，没有先验的假设，将需要确定哪些受众特征来约束信息选择。推荐系统有可能改变关于有效信息的研究。结果将包括（1）有效（戒烟）信息偏好的算法;（2）超越信息设计方法的飞跃，避免了一次一个功能实验中的繁琐工作;（3）采用任何人都熟悉的方法在亚马逊上买过书或通过Netflix选择电影的方法;（4）为自动用户友好的推荐系统奠定基础。增加健康和降低风险的行为的信息选择过程将发生根本性的变化。还将启用使用新媒体的应用程序，如移动的技术和个性化保健网站。研究建议：（1）准备现有数据以用于预测试推荐系统;（2）使用来自商业竞技场的最先进的程序开发推荐算法，所述推荐算法是协作和内容方法的混合;（3）在吸烟者的样本中测试混合算法，将推荐消息的偏好与两个比较条件进行比较;（4）跟进以确定在接收经由推荐器算法建议的消息的那些人与接收随机选择或“最优选”集合的那些人之间戒烟意图的差异是否不同。