We are pleased to congratulate Roblox machine learning engineer Xiao Yu and his co-authors on receiving the Test of Time award at the 17th ACM International Conference on Web Search and Data Mining (WSDM 2024). The Test of Time Award is a mark of historical impact and recognition that the research has changed the trends and direction of the discipline. It recognizes a research publication from 10 years ago that has had a lasting influence.
The winning paper, “Personalized Entity Recommendation: A Heterogeneous Information Network Approach” was first presented at WSDM 2014, while Yu was a researcher at the University of Illinois at Urbana-Champaign. Yu joined Roblox in 2022 and has worked on natural language, computer vision, large language models, and Generative AI, including our recent work on real-time AI chat translation and real-time voice moderation.
Yu says the award-winning paper “introduces the concept of meta-path-based latent features as the representations for users and items. This was before representation learning became state-of-the-art for recommender systems. Though it predates the widespread use of embeddings in heterogeneous networks and recommender systems, the observations and philosophy presented in this paper inspired many researchers to reexamine this problem and sparked a wave of innovative research in this domain.”
The research published by Yu and colleagues has gained significant recognition over the past decade as recommendation engines have become increasingly ubiquitous. “By incorporating diverse relationship information, our method personalizes recommendations to a greater extent, leading to more accurate, relevant, and customized suggestions for users. This is crucial in today’s information overload scenario, where people are bombarded with irrelevant recommendations,” Yu says.
“Prior to this paper, graph-based hybrid recommender systems often utilized a single type of relationship, like whether a user had purchased a certain item before. This was one of the first approaches to leverage the relationship heterogeneity within a network. By modeling various relationships, the proposed recommender system can capture a richer and more nuanced understanding of user preferences and item characteristics.”
Learn about recent AI research at Roblox here.
