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Recency-Based Updating and Dynamic Management of Contextual Rules

In: Context-Aware Machine Learning and Mobile Data Analytics

Author

Listed:
  • Iqbal H. Sarker

    (Swinburne University of Technology
    Chittagong University of Engineering & Technology)

  • Alan Colman

    (Swinburne University of Technology)

  • Jun Han

    (Swinburne University of Technology)

  • Paul Watters

    (Macquarie University
    Cyberstronomy Pty Ltd)

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.

Suggested Citation

  • Iqbal H. Sarker & Alan Colman & Jun Han & Paul Watters, 2021. "Recency-Based Updating and Dynamic Management of Contextual Rules," Springer Books, in: Context-Aware Machine Learning and Mobile Data Analytics, chapter 0, pages 113-125, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-88530-4_7
    DOI: 10.1007/978-3-030-88530-4_7
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