Mean Average Distance to Resolver: An Evaluation Metric for Ticket Routing in Expert Network

Han, Jianglei; Sun, Aixin

doi:10.1109/icsme.2017.18

Cited by 4 publications

(1 citation statement)

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“…Given n, more tickets is resolved, the better a system is. Mean Average Distance to Resolver (MADR@k) [34] scores each routing step with respect to the ground truth routing sequence. Formally,…”

Section: Experiments Setupmentioning

confidence: 99%

UFTR: A Unified Framework for Ticket Routing

Han,

Li,

Sun

2020

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Corporations today face increasing demands for the timely and effective delivery of customer service. This creates the need for a robust and accurate automated solution to what is formally known as the ticket routing problem. This task is to match each unresolved service incident, or â ȂIJticketâ Ȃİ , to the right group of service experts. Existing studies divide the task into two independent subproblems â Ȃ Ş initial group assignment and inter-group transfer. However, our study addresses both subproblems jointly using an end-to-end modeling approach. We first performed a preliminary analysis of half a million archived tickets to uncover relevant features. Then, we devised the UFTR, a Unified Framework for Ticket Routing using four types of features (derived from tickets, groups, and their interactions). In our experiments, we implemented two ranking models with the UFTR. Our models outperform baselines on three routing metrics. Furthermore, a post-hoc analysis reveals that this superior performance can largely be attributed to the features that capture the associations between ticket assignment and group assignment. In short, our results demonstrate that the UFTR is a superior solution to the ticket routing problem because it takes into account previously unexploited interrelationships between the group assignment and group transfer problems.

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Section: Experiments Setupmentioning

confidence: 99%