Mountain View, USA
New York, USA
Mountain View, USA
Los Gatos, USA
The demand for personalized video recommendations has grown exponentially with the widespread use of video content across various domains, including entertainment, e-commerce, education and social media. The explosive growth of video content on the internet, combined with the ubiquitous availability of high-speed internet and advancements in mobile camera technology have made it easier than ever for users to create, access and consume videos. With the proliferation of online social media applications like Instagram, YouTube, Facebook and TikTok, the need for large-scale video recommendation systems which can provide users with personalized and relevant recommendations has increased.
The Large-Scale Video Recommendations workshop (VideoRecSys) acknowledges the vital significance of these systems and the unique challenges and opportunities they present. With the explosive growth of video platforms and the diverse array of user behaviors and preferences, addressing scalability, diversity and serendipity in recommendations becomes a complex yet vital endeavor.
Join us in this workshop where we bring together renowned researchers and industry experts in the field to delve into the latest advancements, cutting-edge techniques and innovative approaches that are shaping the future of large-scale video recommender systems. Through insightful discussions, engaging presentations and collaborative networking, we aim to foster a deeper understanding of the field's intricacies and collectively chart a course towards more effective, responsible and impactful video recommendations.
|14:00-14:15 SGT||Opening Remarks [Slides]|
|14:15-14:45 SGT|| Keynote: YouTube Discovery Evolution [Slides]
Lukasz Heldt, Google
|14:50-15:20 SGT|| Foundational Models for Long Range Interactions History Modeling [Slides]
Thomas Bredillet, Instagram
|15:20-16:05 SGT||Coffee Break Networking|
|16:05-16:35 SGT|| Intents and Journeys: An LLM Approach [Slides]
Minmin Chen, Google DeepMind
|16:35-17:05 SGT|| From Stranger Things to Your Things: Netflix's Recommendation Evolution [Slides]
Ko-Jen (Mark) Hsiao, Netflix
|17:05-17:35 SGT|| Reinforcement Learning for Short Video Recommender Systems [Slides]
Qingpeng Cai, KuaiShou