Bee-Chung Chen


Software Engineer, Sr Machine Learning Architect


I have been a key designer of recommendation algorithms used in LinkedIn and Yahoo!, which power the LinkedIn news feed, job recommendation, article recommendation and key Yahoo! sites including Yahoo! homepage, Yahoo! News and others. My research in this field resulted in more than 20 academic papers published in CACM, KDD, WWW, WSDM, ICDM, EMNLP, etc. In addition to recommender systems, my experience includes utilizing machine learning and statistical modeling to improve data analysis capabilities of data warehouses, and privacy analysis in data publication, which also resulted in more than 10 papers published in conferences like VLDB, SIGMOD and journals like TKDD, VLDB J. and DMKD.

Talks and Events

2022 Talk: Deep Graph Learning For Search And Recommender Systems

Most state-of-the-art search and recommender systems use neural networks to learn representations of entities like users, queries and content items. Graph neural networks (GNNs) are a useful tool for learning such a representation not only based on the entity itself, but also its relationships with other entities. In this talk, we describe the architecture of deep-learning-based search and recommender systems, show how to apply GNNs learned from billions of nodes and edges to these systems to improve their performance, and discuss the lessons that we learned from our journey at Pinterest.