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

• (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

• 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
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• Send me your comments, questions and suggestions to marcel.kurovski@gmail.com
• Recsperts Website

In episode 23 of Recsperts, we welcome Yashar Deldjoo, Assistant Professor at the Polytechnic University of Bari, Italy. Yashar's research on recommender systems includes multimodal approaches, multimedia recommender systems as well as trustworthiness and adversarial robustness, where he has published a lot of work. We discuss the evolution of generative models for recommender systems, modeling paradigms, scenarios as well as their evaluation, risks and harms.

We begin our interview with a reflection of Yashar's areas of recommender systems research so far. Starting with multimedia recsys, particularly video recommendations, Yashar covers his work around adversarial robustness and trustworthiness leading to the main topic for this episode: generative models for recommender systems. We learn about their aspects for improving beyond the (partially saturated) state of traditional recommender systems: improve effectiveness and efficiency for top-n recommendations, introduce interactivity beyond classical conversational recsys, provide personalized zero- or few-shot recommendations.
We learn about the modeling paradigms and as well about the scenarios for generative models which mainly differ by input and modelling approach: ID-based, text-based, and multimodal generative models. This is how we navigate the large field of acronyms leading us from VAEs and GANs to LLMs.

Towards the end of the episode, we also touch on the evaluation, opportunities, risks and harms of generative models for recommender systems. Yashar also provides us with an ample amount of references and upcoming events where people get the chance to know more about GenRecSys.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
Don't forget to follow the podcast and please leave a review

• (00:00) - Introduction
• (03:58) - About Yashar Deldjoo
• (09:34) - Motivation for RecSys
• (13:05) - Intro to Generative Models for Recommender Systems
• (44:27) - Modeling Paradigms for Generative Models
• (51:33) - Scenario 1: Interaction-Driven Recommendation
• (57:59) - Scenario 2: Text-based Recommendation
• (01:10:39) - Scenario 3: Multimodal Recommendation
• (01:24:59) - Evaluation of Impact and Harm
• (01:38:07) - Further Research Challenges
• (01:45:03) - References and Research Advice
• (01:49:39) - Closing Remarks

• Yashar Deldjoo on LinkedIn
• Yashar's Website
• KDD 2024 Tutorial: Modern Recommender Systems Leveraging Generative AI: Fundamentals, Challenges and Opportunities
• RecSys 2024 Workshop: The 1st Workshop on Risks, Opportunities, and Evaluation of Generative Models in Recommender Systems (ROEGEN@RECSYS'24)

Papers:

• Deldjoo et al. (2024): A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
• Deldjoo et al. (2020): Recommender Systems Leveraging Multimedia Content
• Deldjoo et al. (2021): A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial Networks
• Deldjoo et al. (2020): How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models
• Liang et al. (2018): Variational Autoencoders for Collaborative Filtering
• He et al. (2016): Visual Bayesian Personalized Ranking from Implicit Feedback

General Links:

• Follow me on LinkedIn
• Follow me on X
• Send me your comments, questions and suggestions to marcel.kurovski@gmail.com
• Recsperts Website