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Enhancing Customer Service with Multi-Focal Learning Techniques

This paper explores the concept of multi-focal learning and its application in customer service support. It addresses the diverse problem descriptions provided by customers of various backgrounds, proposing a formalized methodology where training data is partitioned into distinct focal groups. Experiments demonstrate that this approach improves learning performance by mitigating the influence of inherent data diversities. Results indicate that both correlation and ontology-based grouping methodologies outperform traditional methods, making multi-focal learning a valuable tool for enhancing the accuracy of customer problem classification.

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Enhancing Customer Service with Multi-Focal Learning Techniques

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  1. Multi-focal Learning and Its Application to Customer Service Support Presenter : Tsai TzungRuei Authors : Yong Ge, HuiXiong, Wenjun Zhou, RamendraSahoo,XiaofengGao,Weili Wu 國立雲林科技大學 National Yunlin University of Science and Technology 2009.SIGKDD

  2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Discussions • Comments

  3. Motivation • All the problem descriptions for the same problem are provided by customers with diverse background and these problem descriptions can be quite different.

  4. Objective • To 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. focal groups Problem descriptions Problem Solution

  5. Methodology(1/3)

  6. Methodology(2/3) • Focal Group Formation:CORRELATION • Focal Group Formation: ONTOLOGY

  7. Methodology(3/3) • Risk Analysis of Multi-Focal Learning

  8. Experiments(1/5) • Results on Problem Logs • Performance Comparisons • Results on Synthetic Data • Case Study

  9. Experiments(2/5) • Results on Problem Logs

  10. Experiments(3/5) • Performance Comparisons

  11. Experiments(4/5) • Results on Synthetic Data

  12. Experiments(5/5) • Case Study

  13. Conclusion • The multi-focal learning allows the learning algorithms to mitigate the influence of the diversities inherent in training data, and thus leads to better learning performances. • Experimental results show that both CORRELATIONand ONTOLOGY have led to better learning performancesthan other focal-group formation methods, suchas the methods based on clustering and random-partition,while the learning performance by ONTOLOGY is lightlybetter than that by CORRELATION.

  14. Discussions • For instance, let us consider a videosurveillance system. There are different types of moving objects,such as cars, bikes, and human beings. Those movingobjects have different sizes, speed, and moving capabilities.To better capture abnormal moving patterns, it is expectedto apply the multi-focal learning techniques to first groupmoving objects into different focal groups. The detectionof abnormal moving patterns can then be performed withindifferent focal groups.

  15. Comments • Advantage • To boost the learning accuracies of existing learning algorithms, such as Support Vector Machines (SVMs), for classifying customer problems. • Drawback • Some mistakes • Application • Customer Service Support

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