Multi-focal learning and its application to customer service support

Yong Ge, Hui Xiong, Wenjun Zhou, Ramendra Sahoo, Xiaofeng Gao, Weili Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this study, we formalize a multi-focal learning problem, where training data are partitioned into several different focal groups and the prediction model will be learned within each focal group. The multi-focal learning problem is motivated by numerous real-world learning applications. For instance, for the same type of problems encountered in a customer service center, the problem descriptions from different customers can be quite different. The experienced customers usually give more precise and focused descriptions about the problem. In contrast, the inexperienced customers usually provide more diverse descriptions. In this case, the examples from the same class in the training data can be naturally in different focal groups. As a result, it is necessary to identify those natural focal groups and exploit them for learning at different focuses. The key developmental challenge is how to identify those focal groups in the training data. As a case study, we exploit multi-focal learning for profiling problems in customer service centers. The results show that multifocal learning can significantly boost the learning accuracies of existing learning algorithms, such as Support Vector Machines (SVMs), for classifying customer problems.

Original languageEnglish (US)
Title of host publicationKDD '09
Subtitle of host publicationProceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages349-357
Number of pages9
DOIs
StatePublished - 2009
Externally publishedYes
Event15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09 - Paris, France
Duration: Jun 28 2009Jul 1 2009

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09
Country/TerritoryFrance
CityParis
Period6/28/097/1/09

Keywords

  • Customer service support
  • Multi-focal learning

ASJC Scopus subject areas

  • Software
  • Information Systems

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