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Contrast Pattern Based Collaborative Behavior Recommendation for Life Improvement

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 10235)

Abstract

Positive attitudes and happiness have major impacts on human health and in particular recovery from illness. While contributing factors leading human beings to positive emotional states are studied in psychology, the effects of these factors vary and change from one person to another. We propose a behaviour recommendation system that recommends the most effective behaviours leading users with a negative mental state (i.e. unhappiness) to a positive emotional state (i.e., happiness). By leveraging the contrast pattern mining framework, we extract the common contrasting behaviours between happy and unhappy users. These contrast patterns are aligned with user behaviours and habits. We find the personalized behaviour recommendation for those with negative emotional states by placing the problem into the nearest neighborhood collaborative filtering framework. A real dataset of people with heart disease or diabetes is used in our recommendation system. The experiments conducted show that the proposed method can be effective in the health-care domain.

Keywords

Recommendation System Latent Dirichlet Allocation Contrast Ratio Contrast Pattern Effective Behaviour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work is funded by the Big Data Research, Analytics, and Information Network (BRAIN) Alliance (established by the Ontario Research Fund - Research Excellence Program), Manifold Data Mining Inc., and Natural Sciences and Engineering Research Council of Canada (NSERC). We would like to thank Manifold for providing the dataset used in this research. In particular, we thank Ted Hains of Manifold and Jianhong Wu of York University for their insights and collaboration in our joint project.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceYork UniversityTorontoCanada
  2. 2.Manifold Data Mining Inc.TorontoCanada

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