scholarly journals TOWARD A COLLECTIVE INTELLIGENCE RECOMMENDER SYSTEM FOR EDUCATION

2017 ◽  
Author(s):  
Jaime Meza ◽  
Leticia Vaca ◽  
Ester Símo Simó ◽  
Josep M. Monguet
Keyword(s):  
Recommender System ◽  
Collective Intelligence

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

10.2196/18064 ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. e18064
Author(s):  
Jamie M Faro ◽  
Catherine S Nagawa ◽  
Jeroan A Allison ◽  
Stephenie C Lemon ◽  
Kathleen M Mazor ◽  
...  
Keyword(s):  
Smoking Cessation ◽  
African American ◽  
African Americans ◽  
Experimental Design ◽  
Recommender System ◽  
Collective Intelligence ◽  
American People ◽  
African American People ◽  
White People ◽  
Quasi Experimental

Background The Patient Experience Recommender System for Persuasive Communication Tailoring (PERSPeCT) is a machine learning recommender system with a database of messages to motivate smoking cessation. PERSPeCT uses the collective intelligence of users (ie, preferences and feedback) and demographic and smoking profiles to select motivating messages. PERSPeCT may be more beneficial for tailoring content to minority groups influenced by complex, personally relevant factors. Objective The objective of this study was to describe and evaluate the use of PERSPeCT in African American people who smoke compared with white people who smoke. Methods Using a quasi-experimental design, we compared African American people who smoke with a historical cohort of white people who smoke, who both received up to 30 emailed tailored messages over 65 days. People who smoke rated the daily message in terms of perceived influence on quitting smoking for 30 days. Our primary analysis compared daily message ratings between the two groups using a t test. We used a logistic model to compare 30-day cessation between the two groups and adjusted for covariates. Results The study included 119 people who smoke (African Americans, 55/119; whites, 64/119). At baseline, African American people who smoke were significantly more likely to report allowing smoking in the home (P=.002); all other characteristics were not significantly different between groups. Daily mean ratings were higher for African American than white people who smoke on 26 of the 30 days (P<.001). Odds of quitting as measured by 30-day cessation were significantly higher for African Americans (odds ratio 2.3, 95% CI 1.04-5.53; P=.03) and did not change after adjusting for allowing smoking at home. Conclusions Our study highlighted the potential of using a recommender system to personalize for African American people who smoke. Trial Registration ClinicalTrials.gov NCT02200432; https://clinicaltrials.gov/ct2/show/NCT02200432 International Registered Report Identifier (IRRID) RR2-10.2196/jmir.6465

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

2020 ◽  
Author(s):  
Jamie M Faro ◽  
Catherine S Nagawa ◽  
Jeroan A Allison ◽  
Stephenie C Lemon ◽  
Kathleen M Mazor ◽  
...  
Keyword(s):  
Smoking Cessation ◽  
African American ◽  
African Americans ◽  
Experimental Design ◽  
Recommender System ◽  
Collective Intelligence ◽  
American People ◽  
African American People ◽  
White People ◽  
Quasi Experimental

BACKGROUND The Patient Experience Recommender System for Persuasive Communication Tailoring (PERSPeCT) is a machine learning recommender system with a database of messages to motivate smoking cessation. PERSPeCT uses the collective intelligence of users (ie, preferences and feedback) and demographic and smoking profiles to select motivating messages. PERSPeCT may be more beneficial for tailoring content to minority groups influenced by complex, personally relevant factors. OBJECTIVE The objective of this study was to describe and evaluate the use of PERSPeCT in African American people who smoke compared with white people who smoke. METHODS Using a quasi-experimental design, we compared African American people who smoke with a historical cohort of white people who smoke, who both received up to 30 emailed tailored messages over 65 days. People who smoke rated the daily message in terms of perceived influence on quitting smoking for 30 days. Our primary analysis compared daily message ratings between the two groups using a <i>t</i> test. We used a logistic model to compare 30-day cessation between the two groups and adjusted for covariates. RESULTS The study included 119 people who smoke (African Americans, 55/119; whites, 64/119). At baseline, African American people who smoke were significantly more likely to report allowing smoking in the home (<i>P</i>=.002); all other characteristics were not significantly different between groups. Daily mean ratings were higher for African American than white people who smoke on 26 of the 30 days (<i>P</i>&lt;.001). Odds of quitting as measured by 30-day cessation were significantly higher for African Americans (odds ratio 2.3, 95% CI 1.04-5.53; <i>P</i>=.03) and did not change after adjusting for allowing smoking at home. CONCLUSIONS Our study highlighted the potential of using a recommender system to personalize for African American people who smoke. CLINICALTRIAL ClinicalTrials.gov NCT02200432; https://clinicaltrials.gov/ct2/show/NCT02200432 INTERNATIONAL REGISTERED REPORT RR2-10.2196/jmir.6465

An Approach to Design and Development Recommender System

2018 ◽  
Vol 6 (3) ◽  
pp. 431-433
Author(s):  
Samir N Ajani ◽  
◽  
Lokesh M Heda ◽  
Santosh Kumar Sahu ◽  
Manish M Motghare ◽  
...  
Keyword(s):  
Recommender System ◽  
Design And Development
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