Online Learning Methods for Large-Scale Recommender Systems
Online learning algorithms have demonstrated a lot of successes in many application domains, ranging from computer vision to text mining and beyond. Despite its wide range of applications, relatively less research has been conducted to leverage the power of online learning methods in large-scale recommender systems. In this presentation, I will first introduce online learning algorithms with application to recommender systems in order to accelerate the training procedure and address some well-known challenges such as cold-start and concept drifting issues. In addition, I will also apply online learning algorithms for resolving another scenario of recommender systems where rich side information is available. I will discuss some open challenges when online learning meets recommender systems.
About the speaker
Chenghao Liu, currently is a Research Fellow with Prof Jianling Sun at Zhejiang University since April 2017. Prior to that, he did his Ph.D degree from September 2011 to March 2017 from Zhejiang University. He was a Visiting Research Assistant at SMU LARC under the supervision of Prof Steven Hoi from September 2014 to September 2016. His research interests span from online learning, convex optimization, to deep learning and various kinds of data mining applications. Currently, he has been actively working on research topics including online learning, deep learning, recommender systems, and stochastic optimization algorithms.
|LARC is supported by the National Research Foundation, Prime Minister's Office, Singapore under its International Research Centres in Singapore Funding Initiative.|
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