  The growing popularity of Virtual Assistants poses new challenges for Entity
Resolution, the task of linking mentions in text to their referent entities in
a knowledge base. Specifically, in the shopping domain, customers tend to use
implicit utterances (e.g., "organic milk") rather than explicit names, leading
to a large number of candidate products. Meanwhile, for the same query,
different customers may expect different results. For example, with "add milk
to my cart", a customer may refer to a certain organic product, while some
customers may want to re-order products they regularly purchase. To address
these issues, we propose a new framework that leverages personalized features
to improve the accuracy of product ranking. We first build a cross-source
heterogeneous knowledge graph from customer purchase history and product
knowledge graph to jointly learn customer and product embeddings. After that,
we incorporate product, customer, and history representations into a neural
reranking model to predict which candidate is most likely to be purchased for a
specific customer. Experiments show that our model substantially improves the
accuracy of the top ranked candidates by 24.6% compared to the state-of-the-art
product search model.
