The School of Informatics and Computing (SoIC) Data Science Talk
Speaker: LingJie Hong, Data Science at Etsy
Where: Lindley Hall, Rm. 102
Topic: A Gradient-based Adaptive Learning Framework for Efficient Personal Recommendation
Abstract: Recommending personalized content to users is a long-standing challenge to many online services, including Facebook, Yahoo!, LinkedIn and Twitter. Traditional recommendation models such as latent factor models and feature-based models are usually trained for all users and optimize an “average” experience for them, yielding sub-optimal solutions. Although multi-task learning provides an opportunity to learn personalized models per user, learning algorithms are often tailored to specific models (e.g., generalized linear model, matrix factorization), creating obstacles for a unified engineering interface, which is important for large Internet companies. This talk will present an empirical framework to learn user-specific personal models for content recommendation by utilizing gradient information from a global model, which potentially benefits any model that can be optimized through gradients, offering a lightweight yet generic alternative to conventional multi-task learning algorithms for user personalization. The effectiveness of the proposed framework is demonstrated by incorporating it in three popular machine learning algorithms including logistic regression, gradient boosting decision tree, and matrix factorization. An extensive empirical evaluation shows a significant improvement in the efficiency of personalized recommendations in real-world datasets.
Biography: 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 from 2013 to 2016, leading science efforts for Personalization and Search Sciences. Liangjie has published papers in all major international conferences in data mining, machine learning and information retrieval, such as SIGIR, WWW, KDD, CIKM, AAAI, WSDM, RecSys and ICML, winning WWW 2011 Best Poster Paper Award, WSDM 2013 Best Paper Nominated and RecSys 2014 Best Paper Award, as well as serving as a program committee member in KDD, WWW, SIGIR, WSDM, AAAI, EMNLP, ICWSM, ACL, CIKM, IJCAI and several workshops. In addition, he constantly reviews articles in prestigious journals such as DMKD, TKDD, TIST, TIS, and TKDE. Liangjie co-founded the User Engagement Optimization Workshop, which has been held in conjunction with CIKM 2013 and KDD 2014. Prior to Yahoo Research, he obtained his Ph.D. (2013) and M.S. (2010) from Lehigh University and B.S. (2007) from Beijing University of Chemical Technology, all in Computer Science.