Name
Abstract | ||
|---|---|---|
Every year tenths of thousands of customer support engineers around the world deal with, and proactively solve, complex help-desk tickets. Daily, almost every customer support expert will turn his/her attention to a prioritization strategy, to achieve the best possible result. To assist with this, in this paper we describe a novel case-based reasoning application to address the tasks of: high solution accuracy and shorter prediction resolution time. We describe how appropriate cases can be generated to assist engineers and how our solution can scale over time to produce domain-specific reusable cases for similar problems. Our work is evaluated using data from 5000 cases from the automotive industry. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01081-2_2 | ICCBR |
Keywords | Field | DocType |
Case-based reasoning,Deep learning,Natural language processing | Software engineering,Computer science,Prioritization,Artificial intelligence,Deep learning,Case-based reasoning,Customer support,Machine learning,Automotive industry | Conference |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kareem Amin | 1 | 0 | 3.72 |
| Stelios Kapetanakis | 2 | 15 | 9.79 |
| Klaus-dieter Althoff | 3 | 991 | 147.58 |
| Andreas Dengel | 4 | 1926 | 280.42 |
| Miltos Petridis | 5 | 165 | 31.65 |