Google
Mountain View, USA
Google
Mountain View, USA
Meta
Menlo Park, USA
Meta
Menlo Park, USA
Meta
Menlo Park, USA
Netflix
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.
Time | Talk |
---|---|
09:05-09:35 CEST | Keynote: Putting the "You" in YouTube: Better Personalization through Larger Models [Slides]
Lexi Baugher Youtube |
14:00-14:05 CEST | Opening Remarks [Slides] |
14:05-14:30 CEST | Topic: Actions Speak Louder than Words: Building the next generation recommendation systems [Slides]
Jiaqi Zhai, Yueming Wang Meta |
14:30-15:00 CEST | Topic: Data and Model Efficiency for RecSys [Slides]
Derek Cheng, Google |
15:00-15:30 CEST | Topic: Evolving Recsys Inference at Meta [Slides]
Lu Fang, Meta |
15:30-15:45 CEST | Coffee Break and Networking |
15:45-16:15 CEST | Topic: Recommending Live Content at Netflix [Slides]
Vito Ostuni, Netflix |
16:15-17:00 CEST | Keynote: The Future of Discovery Assistance [Slides]
Ed Chi, Google Deepmind |
17:00-17:30 CEST |
Panel Discussion Ed Chi, Google Deepmind Justin Basilico, Netflix Chen Zheng, Roblox Jiajing Xu, Pinterest |