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.
翻译:在Alexa中,准确反映客户行为的展示是提供个人化技能路由经验的重要任务。目前,动态路由(DR)团队负责将Alexa的交通路线引导到供应商或技能,它依赖两个特征作为个人信号:绝对交通量计和每个客户每种技能使用的正常交通量计。它们都不考虑客户之间互动和技能之间互动的网络基础结构,其中含有更丰富的客户偏好信息。在这项工作中,我们首先构建一个基于客户过去与所援引技能的互动的差别性边缘分级图表,其中用户请求(特惠)以边缘为模型。然后,我们提出一个基于图形革命网络(GCN)的模型,即个性化动态运行功能图谱仪Encoder(PDRFE),该模型将产生从构建图中学习的个人化客户表现。与现有模型相比,PDFEFE能够进一步获取图形革命函数中的背景资料。我们拟议模型的性能性能通过下游任务、缺陷预测来评估,预测用户请求(特惠)的缺陷标签,从而预测从所学的客户嵌嵌入式嵌入者及其触发的技能。我们所建基准的模型的反向41。我们观测到所建基准的改进了41。