March 27, 2017: Seminar - Yongfeng Zhang: "Personalized Data-Driven Systems"


Personalized Data-Driven Systems

Yongfeng Zhang
University of Massachusetts - Amherst
March 27, 2017
11:00 a.m., Room 1000 SEO


Recent years have witnessed a prospering of data-driven systems, such as e-commerce, social networks, online learning, digital health, and sharing economy applications. These systems have accumulated a large amount of user-generated data, which help to personalize the user preferences, understand their information needs, and provide satisfactory experience for the users. However, the data can come in very heterogeneous and extremely unstructured forms, such as free-texts, ratings, click series, images, or videos, which makes it a difficult task to profile the users for personalized services.

In this talk, I will introduce data-driven techniques for personalized recommendation and information retrieval systems, which include 1) Leveraging sentiment analysis on textual reviews for explainable recommendation; 2) Modeling the shifting of user preferences for dynamic recommendation; 3) Unified representation learning from heterogeneous data sources for multi-view preference modeling; and 4) The economic nature of recommender systems and Web applications. As a conclusion of the talk, I will also provide my future vision on personalization theories for broader and emerging application scenarios, such as personalized education, personalized healthcare, NLP for recommendation, and privacy-preserving recommendation systems.


Yongfeng Zhang is a Postdoc Research Associate in Computer Science at UMass Amherst. His research interest is on Machine Learning and Data Science spanning a range of domains, including Personalization Theories, Information Retrieval, Question Answering, and Computational Economics. He obtained his PhD and BE degree in Computer Science from Tsinghua University, and BS in Economics from Peking University. Before joining UMass Amherst, he was an Assistant Specialist in Computer Science at UC Santa Cruz, and a visiting researcher in School of Computing at the National University of Singapore. He has been regularly publishing on top conferences including WWW, SIGIR, AAAI, IJCAI, WSDM, and many others, and has been serving as program committee members in WWW, SIGIR, IJCAI, WSDM, CIKM, etc. He is a Siebel Scholar and a Baidu Scholar, and his research was funded by the Microsoft PhD Fellowship, IBM PhD Fellowship, and Google Research Fellowships.

Copyright 2016 The Board of Trustees
of the University of
Helping Women Faculty Advance
Funded by NSF