Personalized Entity Resolution with Dynamic Heterogeneous K nowledge G raph Representations (2021)

Abstract: 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 mention the entities implicitly (e.g., “organic milk”) rather than use the entity names explicitly, 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 product from his/her favorite brand, while some customers may want to re-order products they regularly purchase. Moreover, new customers may lack persistent shopping history, which requires us to enrich the connections between customers through products and their attributes. 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 by a specific customer. Experiment results 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.

## introduction given an entity mention as a query, the goal of entity resolution (or entity linking) @xcite is to link the mention to its corresponding entry in a target knowledge base (kb). in an academic shared task setting, an entity mention is usually a name string, which can be a person, organization or geo-political entity in a news context, and the kb is usually a wikipedia dump with rich structured properties and unstructured text descriptions. state-of-the-art entity resolution methods can achieve higher than 90% accuracy in such settings @xcite @xcite ), and they have been successfully applied in hundreds of languages @xcite and various domains such as disaster management @xcite and scientific discovery @xcite . therefore, we tend to think entity resolution is a solved problem in academia. however, in industry, with the rise in popularity of virtual assistants (vas) in recent years, an increasing number of customers now rely on vas to perform daily tasks involving entities, including shopping, playing music or movies, calling a person, booking a flight, and managing schedules. the scale and complexity of industrial applications presents the following unique new challenges. ## methodology given a query q from a customer c, and a list of candidate products @xmath0, ..., p l }, where l is the number of candidates, our goal is to predict the product that the customer will purchase based on their purchase history and the product knowledge graph. specifically, we use purchase records {r 1 , ..., r h } where h is the number of historical records. as figure 2 illustrates, we jointly learn customer and product embeddings from a cross-source customer-product graph using gnn. to perform personalized ranking, we incorporate the learned customer embedding and history representation as additional features when calculating the confidence score of each candidate. we then rank all candidates by confidence score and return the top one. history encoder candidate encoder query candidate customer query embedding query encoder history representation candidate representation scorer customer purchase history customer embedding candidate score ## conclusion and future work we propose a novel framework to jointly learn customer and product representations based on a crosssource heterogeneous graph constructed from customers' purchase history and the product knowledge graph to improve personalized entity resolution. experiments show that our framework can effectively increase the purchase rate of the top ranked products. in the future, we plan to investigate better approaches to integrating personalized features and extend the framework to cross-lingual cross-media settings and generate conversations for more proactive and explainable entity recommendation and summarization.