Outcome aware ranking in interaction networks

TLDR: A novel ranking technique that was developed in the context of an application that arose in a Service Delivery setting based on extension of eigen value methods and results on real-life, public domain datasets from the Internet Movie DataBase are presented.

Abstract: In this paper, we present a novel ranking technique that we developed in the context of an application that arose in a Service Delivery setting. We consider the problem of ranking agents of a service organization. The service agents typically need to interact with other service agents to accomplish the end goal of resolving customer requests. Their ranking needs to take into account two aspects: firstly, their importance in the network structure that arises as a result of their interactions, and secondly, the value generated by the interactions involving them. We highlight several other applications which have the common theme of ranking the participants of a value creation process based on the network structure of their interactions and the value generated by their interactions. We formally present the problem and describe the modeling technique which enables us to encode the value of interaction in the graph. Our ranking algorithm is based on extension of eigen value methods. We present experimental results on real-life, public domain datasets from the Internet Movie DataBase. This makes our experiments replicable and verifiable.

Figures

Table 2: Ranking for different α values

Figure 5: A star interaction network with 21 agents and 5 outcomes

Figure 6: Three different utilities

Figure 7: Trade-off of interactions and outcomes for utilities U1, U2, and U3

Table 1: Rankings based on different methodologies

Figure 1: Individual interactions with illustrative outcomes from different communities

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In this paper, the authors present a novel ranking technique that they developed in the context of an application that arose in a Service Delivery setting. The authors consider the problem of ranking agents of a service organization. The authors formally present the problem and describe the modeling technique which enables us to encode the value of interaction in the graph. The authors present experimental results on real-life, public domain datasets from the Internet Movie DataBase.