  Generating representations that precisely reflect customers' behavior is an
important task for providing personalized skill routing experience in Alexa.
Currently, Dynamic Routing (DR) team, which is responsible for routing Alexa
traffic to providers or skills, relies on two features to be served as personal
signals: absolute traffic count and normalized traffic count of every skill
usage per customer. Neither of them considers the network based structure for
interactions between customers and skills, which contain richer information for
customer preferences. In this work, we first build a heterogeneous edge
attributed graph based customers' past interactions with the invoked skills, in
which the user requests (utterances) are modeled as edges. Then we propose a
graph convolutional network(GCN) based model, namely Personalized Dynamic
Routing Feature Encoder(PDRFE), that generates personalized customer
representations learned from the built graph. Compared with existing models,
PDRFE is able to further capture contextual information in the graph
convolutional function. The performance of our proposed model is evaluated by a
downstream task, defect prediction, that predicts the defect label from the
learned embeddings of customers and their triggered skills. We observe up to
41% improvements on the cross entropy metric for our proposed models compared
to the baselines.
