Liangjie Hong
Head of Data Science
Etsy, Inc.

"GB-CENT: Gradient Boosted Categorical Embedding and Numerical Trees "

Wednesday, April 12, 4:00 PM
Packard Lab 466

Abstract:   Latent factor models and decision tree based models are widely used in tasks of prediction, ranking and recommendation. Latent factor models have the advantage of interpreting categorical features by a low-dimensional representation, while such an interpretation does not naturally fit numerical features. In contrast, decision tree based models enjoy the advantage of capturing the nonlinear interactions of numerical features, while their capability of handling categorical features is limited by the cardinality of those features. Since in real-world applications we usually have both abundant numerical features and categorical features with large cardinality (e.g. geolocations, IDs, tags etc.), we design a new model, called GB-CENT, which leverages latent factor embedding and tree components to achieve the merits of both while avoiding their demerits. With two real-world data sets, we demonstrate that GB-CENT can effectively (i.e. fast and accurate) achieve better accuracy than state-of-the-art matrix factorization, decision tree based models and their ensemble.

Bio:   Liangjie Hong is Head of Data Science at Etsy Inc., managing a group of data scientists to deliver cutting-edge scientific solutions for: Search and Discovery, Personalization and Recommendation and Computational Advertising. Previously, he was Senior Manager of Research at Yahoo Research, leading science efforts for Personalization and Search Sciences, driving science solutions for a wide range of products. Over the past several years, Dr. Hong has published papers in all major venues in data mining and machine learning, winning WWW 2011 Best Poster Paper Award, WSDM 2013 Best Paper Nominated and RecSys 2014 Best Paper Award. He has served as program committee members in top conferences like KDD, WWW, SIGIR, WSDM, AAAI, EMNLP, ICWSM, ACL, CIKM, and IJCAI. In addition, Dr. Hong constantly reviews articles in most prestige journals such as DMKD, TKDD, TIST, TIS, and TKDE. Prior to Yahoo Research, Dr. Hong was a research assistant in Department of Computer Science and Engineering at Lehigh University, where he was a member of WUME lab from 2008 to 2013, working with Brian D. Davison. Dr. Hong obtained his Ph.D. (2013), M.S. (2010) from Lehigh University and B.S. (2007) from Beijing University of Chemical Technology, all in Computer Science.

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