Discovering User Behavioral Rules Based on Multi-Dimensional Contexts

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 6 in Context-Aware Machine Learning and Mobile Data Analytics, 2021, pp 93-111 from Springer

Abstract: Abstract In the previous chapter, we have presented an approach for discovering time-dependent rules of individual mobile phone users based on their unique behavioral patterns. In this chapter, we focus on discovering behavioral rules of individual mobile phone users by taking into account multi-dimensional contexts—for example temporal, spatial, or social context. Association rule mining is the most prominent rule-based machine learning method for generating rules for a particular constraint preference utilizing a given dataset. However, it generates various uninteresting contextual associations which lead to output vast number of redundant rules that become ineffective in making context-aware decisions. This redundant generation not only makes the rule set unnecessarily large in size, but also complicates the decision-making process in a context-aware system. In this chapter, we present an effective rule-based machine learning method that minimizes the issue and generates a set of non-redundant behavioral rules by taking into account the precedence of relevant contexts. Finally, the effectiveness of the technique presented in this chapter, has been provided through experimental results.

Keywords: Rule discovery; 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_6

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DOI: 10.1007/978-3-030-88530-4_6

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