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

Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations