Multifocal learning for customer problem analysis

  • Authors:

  • Yong Ge;Hui Xiong;Wenjun Zhou;Siming Li;Ramendra Sahoo

  • Affiliations:

  • Rutgers University, Newark, NJ;Rutgers University, Newark, NJ;Rutgers University, Newark, NJ;State University of New York, Stony Brook;Citigroup Inc.

  • Venue:
  • ACM Transactions on Intelligent Systems and Technology (TIST)
  • Year:
  • 2011

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Abstract

In this study, we formalize a multifocal learning problem, where training data are partitioned into several different focal groups and the prediction model will be learned within each focal group. The multifocal 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. Experienced customers usually give more precise and focused descriptions about the problem. In contrast, inexperienced customers usually provide diverse descriptions. In this case, the examples from the same class in the training data can be naturally in different focal groups. Therefore, it is necessary to identify those natural focal groups and exploit them for learning at different focuses. Along this line, the key development challenge is how to identify those focal groups in the training data. As a case study, we exploit multifocal learning for profiling customer problems. Also, we provide an empirical study about how the performance of multifocal learning is affected by the quality of focal groups. The results on real-world customer problem logs show that multifocal learning can significantly boost the performance of many existing classification algorithms, such as Support Vector Machines (SVMs), for classifying customer problems and there is strong correlation between the quality of focal groups and the learning performance.