Published on: 27-Mar-2020
The paper "HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems", by Lucas Vinh Tran, Yi Tay, Shuai Zhang, Gao Cong, and Xiaoli Li has won Best Paper Award Runner-Up in the 13th ACM International Conference on Web Search and Data Mining (WSDM), 2020 (Acceptance rate of 14.80%, 91 out of 615 submissions). The award was conferred in Houston, Texas from February 3-7, 2020.
SCSE PhD student, Lucas Vinh Tran received the award certificate from the General Chairs of WSDM 2020, Prof James Caverlee (Texas A&M University) and Prof Xia Hu, Ben (Texas A&M University) in Houston, Texas.
WSDM (pronounced "wisdom") is one of the top-tier conferences, publishes original, high-quality papers related to search and data mining on the Web and the Social Web, with an emphasis on practical yet principled novel models of search and data mining, algorithm design and analysis, economic implications, and in-depth experimental analysis of accuracy and performance.
The awarded paper, named “HyperML: A Boosting Metric Learning Approach for Recommender Systems”, introduces a unique and non-trivial approach of representation learning for recommender systems by exploring Hyperbolic space instead of Euclidean space. The paper demonstrates promising results for recommender systems, which may inspire other future work to explore Hyperbolic space in solving recommendation problems. The main author Lucas Vinh Tran is a PhD candidate of SCSE, supervised by Prof Gao Cong (NTU) and Dr Xiaoli Li (I2R, A*STAR).
Back to listing