PERSPeCT: Collaborative Filtering for Tailored Health Communications

被引：11

作者：

Adams, Roy J. ^{[1
]}

Sadasivam, Rajani S. ^{[2
]}

Balakrishnan, Kavitha ^{[2
]}

Kinney, Rebecca L. ^{[2
]}

Houston, Thomas K. ^{[3
]}

Marlin, Benjamin M. ^{[1
]}

机构：

[1] UMass Amherst, Sch Comp Sci, Amherst, MA 01003 USA

[2] UMass Med Sch, Dept Quantitat Hlth Sci, Worcester, MA USA

[3] UMass Med Sch, eHlth QUERI, Bedford VA Med Ctr, Worcester, MA USA

来源：

PROCEEDINGS OF THE 8TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'14) | 2014年

关键词：

Recommender systems; tailored health communications;

D O I：

10.1145/2645710.2645768

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：329 / 332

页数：4

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