Mon, May 07, 2018 @ 01:00 PM - 03:00 PM
PhD Candidate: Kuan Liu
Title: Scalable machine learning algorithms for implicit feedback based recommendation
Committee: Prem Natarajan, Kevin Knight, Shri Narayanan (outside member)
Whether in e-commerce, social networks, online music and TV, and many other modern online services, item recommendation stands out to be one of the most important algorithmic components. It recommends items to users that are useful and relevant. It makes huge economic values and is an important information filtering tool.
The primary goal of this thesis research is to provide machine learning solutions to item recommendation in large scale. The ever-increasing data volume and rich data formats have created a big gap between the requirements of modern recommender systems and our algorithm ability to handle large scale tasks. We work towards efficient personalized ranking algorithms to handle large data volume and advance content-based approaches to incorporate rich side information.
The thesis work mainly focuses on the following aspects towards this goal: (1) Novel ranking algorithms to deal with large itemsets (2) Deep learning methods to model sequential properties of user feedback (3) To incorporate heterogeneous attributes (4) To fuse signals from multiple modalities. In this talk, I will provide a brief overview of item recommendation history and our contributions. I will discuss our recent work on batch-based ranking algorithms for recommendation from large itemsets and our new methods to fuse signals from multiple modalities.
Audiences: Everyone Is Invited
Contact: Lizsl De Leon