Learning Personalized Representations using Graph Convolutional Network
This work addresses the need for more accurate personalized routing in Alexa by incorporating graph-based contextual information, but it is incremental as it builds on existing GCN methods for a specific domain.
The authors tackled the problem of generating personalized customer representations for Alexa skill routing by building a heterogeneous graph of customer-skill interactions and proposing a GCN-based model, PDRFE, which achieved up to 41% improvement in cross entropy for defect prediction compared to baselines.
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.