In episode 24 of Recsperts, I sit down with Amey Dharwadker, Machine Learning Engineering Manager at Facebook, to dive into the complexities of large-scale video recommendations. Amey, who leads the Video Recommendations Quality Ranking team at Facebook, sheds light on the intricate challenges of delivering personalized video feeds at scale. Our conversation covers content understanding, user interaction data, real-time signals, exploration, and evaluation techniques.
We kick off the episode by reflecting on the inaugural VideoRecSys workshop at RecSys 2023, setting the stage for a deeper discussion on Facebook’s approach to video recommendations. Amey walks us through the critical challenges they face, such as gathering reliable user feedback signals to avoid pitfalls like watchbait. With a vast and ever-growing corpus of billions of videos—millions of which are added each month—the cold start problem looms large. We explore how content understanding, user feedback aggregation, and exploration techniques help address this issue. Amey explains how engagement metrics like watch time, comments, and reactions are used to rank content, ensuring users receive meaningful and diverse video feeds.
A key highlight of the conversation is the importance of real-time personalization in fast-paced environments, such as short-form video platforms, where user preferences change quickly. Amey also emphasizes the value of cross-domain data in enriching user profiles and improving recommendations.
Towards the end, Amey shares his insights on leadership in machine learning teams, pointing out the characteristics of a great ML team.
Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
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- (00:00) - Introduction
- (02:32) - About Amey Dharwadker
- (08:39) - Video Recommendation Use Cases on Facebook
- (16:18) - Recommendation Teams and Collaboration
- (25:04) - Challenges of Video Recommendations
- (31:07) - Video Content Understanding and Metadata
- (33:18) - Multi-Stage RecSys and Models
- (42:42) - Goals and Objectives
- (49:04) - User Behavior Signals
- (59:38) - Evaluation
- (01:06:33) - Cross-Domain User Representation
- (01:08:49) - Leadership and What Makes a Great Recommendation Team
- (01:13:01) - Closing Remarks
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