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A Hybrid Framework for a Comprehensive Physical Activity and Diet Recommendation System

  • Syed Imran Ali
  • Muhammad Bilal Amin
  • Seoungae Kim
  • Sungyoung LeeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10898)

Abstract

The quantified self-movement has gained a lot of traction, recently. In this regard, research in personalized wellness support systems has increased. Most of the recommender systems focus on either calorie-burn or calorie-in take objectives. The achievement of calorie-burn objective is through physical activity recommendations while diet recommendations geared towards calorie-in take objectives. A very limited research is performed which track and optimize objectives for both calorie-burn and calorie-in-take, simultaneously based on well-known wellness support guidelines. In this regard, we propose a hybrid recommendation framework, which provides recommendations for physical activity as well as diet recommendation in order to support wellness requirements of a user in a comprehensive manner.

Keywords

Recommender system Self-quantification Wellness support system 

Notes

Acknowledgement

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-0-01629) supervised by the IITP (Institute for Information & communications Technology Promotion)” and by the Korea Research Fellowship Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2016H1D3A1938039).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Syed Imran Ali
    • 1
  • Muhammad Bilal Amin
    • 1
  • Seoungae Kim
    • 1
  • Sungyoung Lee
    • 1
    Email author
  1. 1.Department of Computer Science and EngineeringKyung Hee UniversitySeoulSouth Korea

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