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Testing a Machine Learning–Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial

Testing a Machine Learning–Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial

We evaluated several recommender methods for accurate prediction, including K-nearest neighbors, probabilistic matrix factorization, collective matrix factorization, and the Bayesian probabilistic matrix factorization (BPMF) [33]. In evaluating rating prediction methods, we used a range of standard performance metrics including root mean squared error, Kendall tau-b, and normalized discounted cumulative gain. In all these tests, BPMF was identified as the best single model.

Ariana Kamberi, Benjamin Weitz, Julie Flahive, Julianna Eve, Reem Najjar, Tara Liaghat, Daniel Ford, Peter Lindenauer, Sharina Person, Thomas K Houston, Megan E Gauvey-Kern, Jackie Lobien, Rajani S Sadasivam

JMIR Res Protoc 2025;14:e63693

Comparison of a Collective Intelligence Tailored Messaging System on Smoking Cessation Between African American and White People Who Smoke: Quasi-Experimental Design

Comparison of a Collective Intelligence Tailored Messaging System on Smoking Cessation Between African American and White People Who Smoke: Quasi-Experimental Design

We comparatively tested the classical algorithms k-nearest neighbors, probabilistic matrix factorization, Bayesian probabilistic matrix factorization [BPMF], collective matrix factorization, and Bayesian collective matrix factorization to identify one that provided maximal prediction accuracy (ie, we evaluated the ability of the algorithms to generalize ratings to nontraining users).

Jamie M Faro, Catherine S Nagawa, Jeroan A Allison, Stephenie C Lemon, Kathleen M Mazor, Thomas K Houston, Rajani S Sadasivam

JMIR Mhealth Uhealth 2020;8(4):e18064