Contextual Bandit Learning for Activity-Aware Things-of-Interest Recommendation in an Assisted Living Environment

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Recommendation systems are crucial for providing services to the elderly with Alzheimer’s disease in IoT-based smart home environments. Therefore, we present a Reminder Care System to help Alzheimer patients live safely and independently in their homes. The proposed recommendation system is formulated based on a contextual bandit approach to tackle dynamicity in human activity patterns for accurate recommendations meeting user needs without their feedback. Our experiment results demonstrate the feasibility and effectiveness of the proposed Reminder Care System in real-world IoT-based smart home applications.

Original languageEnglish
Title of host publicationDatabases Theory and Applications - 32nd Australasian Database Conference, ADC 2021, Proceedings
EditorsMiao Qiao, Gottfried Vossen, Sen Wang, Lei Li
PublisherSpringer Science and Business Media Deutschland GmbH
Pages37-49
Number of pages13
ISBN (Print)9783030693763
DOIs
StatePublished - 2021
Externally publishedYes
Event32nd Australasian Database Conference, ADC 2021 - Dunedin, New Zealand
Duration: 29 Jan 20215 Feb 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12610 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference32nd Australasian Database Conference, ADC 2021
Country/TerritoryNew Zealand
CityDunedin
Period29/01/215/02/21

Keywords

  • Contextual bandit
  • IoT
  • Recommender system

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