Context-Induced Activity Monitoring for On-Demand Things-of-Interest Recommendation in an Ambient Intelligent Environment

May Altulyan, Lina Yao, Chaoran Huang, Xianzhi Wang and Salil S. Kanhere
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May Altulyan: School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Lina Yao: School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Chaoran Huang: School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Xianzhi Wang: School of Computer Science, The University of Technology Sydney, Ultimo, NSW 2007, Australia
Salil S. Kanhere: School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia

Future Internet, 2021, vol. 13, issue 12, 1-19

Abstract: Recommendation systems are crucial in the provision of services to the elderly with Alzheimer’s disease in IoT-based smart home environments. In this work, a Reminder Care System (RCS) is presented to help Alzheimer patients live in and operate their homes safely and independently. A contextual bandit approach is utilized in the formulation of the proposed recommendation system to tackle dynamicity in human activities and to construct accurate recommendations that meet user needs without their feedback. The system was evaluated based on three public datasets using a cumulative reward as a metric. Our experimental results demonstrate the feasibility and effectiveness of the proposed Reminder Care System for real-world IoT-based smart home applications.

Keywords: contextual bandit; IoT; recommender system (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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