Trends in Personalized Video Recommendations

Modern personalized video recommender systems optimize for multiple objectives and stakeholders in a real-world system. To do this effectively, such systems utilize multi-task learning models to rank both long and short videos more accurately and provide the most relevant content tailored to users' interests. This talk will focus on recent machine learning trends in personalized video recommendations and how they have emerged as key elements to address various challenges in this space. We will provide a comprehensive overview of different approaches to develop more sophisticated and effective methods to increase user engagement and user satisfaction in large-scale video recommender systems.

Who is this presentation for?
The presentation is intended for practitioners, industry professionals and researchers working in the field of machine learning, recommender systems, and video recommendations. Attendees of the talk may include engineers, data scientists, product managers, and researchers who are interested in learning about recent trends and advancements in personalized video recommendations and how these advancements are addressing various challenges in the field.

Prerequisite knowledge:
A very basic understanding of machine learning and recommender systems would be helpful for attendees to fully understand the content of the talk.

What you'll learn?
(1) What are the various challenges in building modern personalized video recommender systems? (2) How have recent machine learning (ML) trends enabled us to address these challenges? (3) What are some recent ML approaches to increase user engagement and user satisfaction in large-scale video recommender systems?

Amey has over a decade of experience in AI and ML, both from an academic and industrial standpoint. He works as a Machine Learning Technical Lead at Meta, leading the Facebook Video Recommendations Core Ranking team building personalized models for billions of users. He has also been instrumental in driving a significant increase in user engagement and revenue for the company through his work on News Feed and Ads ranking. He has several international publications and patents in the fields of recommender systems and has served as a program committee member for top ML conferences and journals. He is often invited to speak at events and provide expert insights and advice on AI and ML in prominent technology publications. As a thought leader, he is committed to mentoring and advising ML engineers, data scientists and early-stage companies through his involvement in hackathons, angel syndicates and startup accelerators. He also serves on the juries of renowned global technology competitions, including the Edison Awards. He was honored with the International Achievers' Award, Scientist of the Year Award and Most Prominent Industry Expert of the Year Award in the Machine Learning category for his exceptional professional accomplishments.