#24: Video Recommendations at Facebook with Amey Dharwadker
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.
Don't forget to follow the podcast and please leave a review
Links from the Episode:
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.
Don't forget to follow the podcast and please leave a review
- (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
Links from the Episode:
- Amey Dharwadker on LinkedIn
- Amey's Website
- RecSys Challenge 2021
- VideoRecSys Workshop 2023
- VideoRecSys + LargeRecSys 2024
Papers:
- Mahajan et al. (2023): CAViaR: Context Aware Video Recommendations
- Mahajan et al. (2023): PIE: Personalized Interest Exploration for Large-Scale Recommender Systems
- Raul et al. (2023): CAM2: Conformity-Aware Multi-Task Ranking Model for Large-Scale Recommender Systems
- Zhai et al. (2024): Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
- Saket et al. (2023): Formulating Video Watch Success Signals for Recommendations on Short Video Platforms
- Wang et al. (2022): Surrogate for Long-Term User Experience in Recommender Systems
- Su et al. (2024): Long-Term Value of Exploration: Measurements, Findings and Algorithms
General Links:
- Follow me on LinkedIn
- Follow me on X
- Send me your comments, questions and suggestions to marcel.kurovski@gmail.com
- Recsperts Website
