When LLMs Meet Recommendations: Scalable Hybrid Approaches to Enhance User Experiences
The Data Science Discipline of the School of EECS is hosting the following guest seminar:
When LLMs Meet Recommendations: Scalable Hybrid Approaches to Enhance User Experiences
Speaker: Dr Jianling Wang (Google Deepmind)
Host: Dr Ruihong Qiu
Abstract: While LLMs offer powerful reasoning and generalization capabilities for user understanding and long-term planning in recommendation systems, their latency and cost hinder direct application in large-scale industrial settings. The talk will cover our recent work on scalable hybrid approaches that combine LLMs and traditional recommendation models. We’ll explore their effectiveness in tackling challenges like cold-start recommendations and enhancing user exploration.
Speaker Bio: Jianling Wang is a senior research scientist working at Google DeepMind. She obtained her Ph.D. degree from the Department of Computer Science and Engineering at Texas A&M University, advised by Prof. James Caverlee. Her research interests generally include data mining and machine learning, with a particular focus on recommendation systems and graph neural networks.
About Data Science Seminar
This seminar series is hosted by EECS Data Science.