Recency-Based Updating and Dynamic Management of Contextual Rules

Iqbal H. Sarker, Alan Colman, Jun Han and Paul Watters
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Iqbal H. Sarker: Swinburne University of Technology
Alan Colman: Swinburne University of Technology
Jun Han: Swinburne University of Technology
Paul Watters: Macquarie University

Chapter Chapter 7 in Context-Aware Machine Learning and Mobile Data Analytics, 2021, pp 113-125 from Springer

Abstract: Abstract In the previous chapter, we have presented an approach for discovering behavioral rules of individual mobile phone users based on multi-dimensional contexts (temporal, spatial, and social context) utilizing their phone log data. However, user behavior is not static, may change over time in the real world. The discovered rules from mobile phone data, therefore, need to be dynamically updated and managed according to the recent behavioral patterns of individual users. The recency-based updates may not only invalidate some existing rules but also make other rules relevant. Therefore, the task of recency-based updating and management of rules for mobile phone users has come to represent an important field of research. In this chapter, we present a recency-based approach for modeling individual’s behavior to resolve this issue.

Keywords: Recent pattern; Rule management; Machine learning; Multi-dimensional contexts; User behavior modeling; Rule-based system (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-88530-4_7

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DOI: 10.1007/978-3-030-88530-4_7

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