Activity Recommendation Model Using Rank Correlation for Chronic Stress Management

Abstract

Korean people are exposed to stress due to the constant competitive structure caused by rapid industrialization. As a result, there is a need for ways that can effectively manage stress and help improve quality of life. Therefore, this study proposes an activity recommendation model using rank correlation for chronic stress management. Using Spearman’s rank correlation coefficient, the proposed model finds the correlations between users’ Positive Activity for Stress Management (PASM), Negative Activity for Stress Management (NASM), and Perceived Stress Scale (PSS). Spearman’s rank correlation coefficient improves the accuracy of recommendations by putting a basic rank value in a missing value to solve the sparsity problem and cold-start problem. For the performance evaluation of the proposed model, F-measure is applied using the average precision and recall after five times of recommendations for 20 users. As a result, the proposed method has better performance than other models, since it recommends activities with the use of the correlation between PASM and NASM. The proposed activity recommendation model for stress management makes it possible to manage user’s stress effectively by lowering the user’s PSS using correlation.

Keywords

• medical data mining
• chronic stress
• correlation
• activity recommendation model
• stress management
• mental healthcare