Constructing Recommender Systems for Effective Health Messages: Smoking Cessation

构建有效健康信息的推荐系统：戒烟

• 批准号: 8159836
• 负责人: Joseph Nicholas Cappella
• 金额: $ 25.81万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-23 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8159836
• 关键词: Algorithms; Antismoking; Archives; Arts; Behavior; Books; Categories; Characteristics; Complex; Data; Databases; Development; Effectiveness; Emotional; Esthesia; Evaluation; Goals; Health; Health Campaign; Health behavior change; Hybrids; Individual; Intention; Knowledge; Knowledge acquisition; Left; Linguistics; Measures; Methods; Modeling; Oral; Outcome; Persons; Procedures; Process; Public Health; Random Allocation; Recommendation; Reporting; Research; Risk; Sampling; Science; Side; Smoker; Staging; Structure; System; Taste Perception; Technology; Testing; Time; Vendor; Visual; Work; base; behavior change; commercial application; design; expectation; falls; interest; meetings; movie; peer; preference; research study; smoking cessation; social; success; theories; user-friendly; visual information; web site

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. PUBLIC HEALTH RELEVANCE: Impact on Public Health Public health campaigns that seek to change behavior are successful in large part when the messages deployed are themselves effective by being attuned to the target audience and -- when possible -- the target individual. Conventional approaches to the design of effective messages in health behavior change -- primarily experimental and factorial in approach -- have moved too slowly both empirically and theoretically leaving message design unprincipled and intuitive. The research proposed radically alters the approach to message design and selection by developing empirically based "recommendation algorithms" based on a model of commercial product recommendation and applied and tested on a large archive of smoking cessation messages.

描述（由申请人提供）：成功的公共卫生运动在很大程度上取决于信息传播的有效性。为公众健康设计有效的信息既是一门艺术，也是一门科学，其中艺术占主导地位，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消息设计的标准实验方法，并放弃了理论发展，转而开发仿照商业系统的“推荐机”。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，针对个人偏好定制消息推荐。 推荐系统本质上是在密集数据上运行的派生算法，涉及消息偏好（吸烟者的评分）和客观消息特征（内容）。他们的目标是预测用户对用户之前未见过的消息的评分。传统的信息研究方法对信息设计科学的推进速度太慢，受到不充分的理论驱动，并且需要受众特征、信息特征和目标行为之间非常复杂的因素相互作用。健康信息推荐机的开发将基于大量信息档案、来自吸烟者的密集偏好数据以及对信息客观特征的广泛（且大部分是自动化的）评估。结果将提供一个从大型档案中选择有效消息的程序，该消息将为特定目标人员量身定制。与定制研究不同，不会对需要识别哪些受众特征来限制消息选择做出先验假设。 推荐系统有潜力改变有效信息的研究。结果将包括 (1) 有效（戒烟）消息偏好的算法； (2) 超越消息设计方法的飞跃，避免了一次一个特征实验中的繁琐工作； (3) 采用任何曾经在亚马逊上购买过书籍或通过 Netflix 选择过电影的人都熟悉的方法； (4) 为自动用户友好推荐系统奠定基础。促进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。使用移动技术和个性化健康网站等新媒体的应用程序也将启用。 研究建议：（1）准备现有数据用于预测试推荐系统； (2) 使用商业领域最先进的程序开发混合协作和内容方法的推荐算法； (3) 在吸烟者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) 跟进以确定接收通过推荐算法建议的消息的人与接收随机选择或“最喜欢”组的人之间戒烟意图的差异是否不同。 公共卫生相关性：对公共卫生的影响 当所传播的信息本身能够有效地适应目标受众（如果可能的话）目标个人时，旨在改变行为的公共卫生运动就会取得成功。设计健康行为改变有效信息的传统方法——主要是实验方法和阶乘方法——在经验和理论上都进展得太慢，导致信息设计无原则和直观。该研究提出的研究通过开发基于商业产品推荐模型的基于经验的“推荐算法”，并在大量戒烟消息档案上进行应用和测试，从根本上改变了消息设计和选择的方法。