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Faculty Candidate Talk
Personalized Data-Driven Systems
Wednesday, April 26, 2017, 10:30am
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, dynamic, and extremely unstructured forms, such as free-texts, ratings, click series, images, or videos, which make it a difficult task to profile the users for personalized services.
In this talk, I will introduce data-driven techniques for personalized recommendation 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 online systems. 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, personalized smart home devices, NLP for recommendation, and privacy-preserving recommendation systems.
Speaker: Yongfeng Zhang
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, Computational Economics, Information Retrie
Location : CoRE A 301
Ahmed Elgammal and Alex Borgida
Event Type: Faculty Candidate Talk