Recsperts - Recommender Systems Experts Marcel Kurovski

In episode number 14 of Recsperts we talk to Daniel Svonava, CEO and Co-Founder of Superlinked, delivering user modeling infrastructure. In his former role he was a senior software engineer and tech lead at YouTube working on ad performance prediction and pricing.
We discuss the crucial role of user modeling for recommendations and discovery. Daniel presents two examples from YouTube’s ad performance forecasting to demonstrate the bandwidth of use cases for user modeling. We also discuss sources of information that fuel user models and additional personlization tasks that benefit from it like user onboarding. We learn that the tight combination of user modeling with (near) real-time updates is key to a sound personalized user experience.
Daniel also shares with us how Superlinked provides personalization as a service beyond ecommerce-centricity. Offering personalized recommendations of items and people across various industries and use cases is what sets Superlinked apart. In the end, we also touch on the major general challenge of the RecSys community which is rebranding in order to establish a more positive image of the field.
Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
Chapters:

(03:35) - Introduction Daniel Svonava
(10:18) - Introduction to User Modeling
(17:52) - User Modeling for YouTube Ads
(35:43) - Real-Time Personalization
(57:29) - ML Tooling for User Modeling and Real-Time Personalization
(01:07:41) - Superlinked as a User Modeling Infrastructure
(01:31:22) - Rebranding RecSys as Major Challenge
(01:37:40) - Final Remarks
Links from the Episode:
Daniel Svonava on LinkedIn
Daniel Svonava on Twitter
Superlinked - User Modeling Infrastructure
The 2023 MAD (Machine Learning, Artificial Intelligence, Data Science) Landscape
Eric Ries: The Lean Startup
Rob Fitzpatrick: The Mom Test
Papers:

Liu et al. (2022): Monolith: Real Time Recommendation System With Collisionless Embedding Table
RSPapers Collection
General Links:

Follow me on Twitter: https://twitter.com/MarcelKurovski

Send me your comments, questions and suggestions to marcel@recsperts.com

Podcast Website: https://www.recsperts.com/

This episode of Recsperts features Justin Basilico who is director of research and engineering at Netflix. Justin leads the team that is in charge of creating a personalized homepage. We learn more about the evolution of the Netflix recommender system from rating prediction to using deep learning, contextual multi-armed bandits and reinforcement learning to perform personalized page construction. Deep content understanding drives the creation of useful groupings of videos to be shown in a personalized homepage.
Justin and I discuss the misalignment of metrics as just one out of many elements that is making personalization still “super hard”. We hear more about the journey of deep learning for recommender systems where real usefulness comes from taking advantage of the variety of data besides pure user-item interactions, i.e. histories, content, and context. We also briefly touch on RecSysOps for detecting, predicting, diagnosing and resolving issues in a large-scale recommender systems and how it helps to alleviate item cold-start.
In the end of this episode, we talk about the company culture at Netflix. Key elements are freedom and responsibility as well as providing context instead of exerting control. We hear that being really comfortable with feedback is important for high-performance people and teams.
Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
Chapters:

(03:13) - Introduction Justin Basilico
(07:37) - Evolution of the Netflix Recommender System
(22:28) - Page Construction of the Personalized Netflix Homepage
(32:12) - Misalignment of Metrics
(37:36) - Experience with Deep Learning for Recommender Systens
(48:10) - RecSysOps for Issue Detection, Diagnosis and Response
(55:38) - Bandits Recommender Systems
(01:03:22) - The Netflix Culture
(01:13:33) - Further Challenges
(01:15:48) - RecSys 2023 Industry Track
(01:17:25) - Closing Remarks
Links from the Episode:
Justin Basilico on Linkedin
Justin Basilico on Twitter
Netflix Research Publications
The Netflix Tech Blog
CONSEQUENCES+REVEAL Workshop at RecSys 2022
Learning a Personalized Homepage (Alvino et al., 2015)
Recent Trends in Personalization at Netflix (Basilico, 2021)
RecSysOps: Best Practices for Operating a Large-Scale Recommender System (Saberian et al., 2022)
Netflix Fourth Quarter 2022 Earnings Interview
No Rules Rules - Netflix and the Culture of Reinvention (Hastings et al., 2020)
Job Posting for Netflix' Recommendation Team
Papers:

Steck et al. (2021): Deep Learning for Recommender Systems: A Netflix Case Study
Steck et al. (2021): Negative Interactions for Improved Collaborative Filtering: Don't go Deeper, go Higher
More et al. (2019): Recap: Designing a more Efficient Estimator for Off-policy Evaluation in Bandits with Large Action Spaces
Bhattacharya et al. (2022): Augmenting Netflix Search with In-Session Adapted Recommendations
General Links:

Follow me on Twitter: https://twitter.com/MarcelKurovski

Send me your comments, questions and suggestions to marcel@recsperts.com

Podcast Website: https://www.recsperts.com/

In episode number 12 of Recsperts we meet Rishabh Mehrotra, the Director of Machine Learning at ShareChat and former Staff Research Scientist & Area Tech Lead at Spotify. We discuss user need, intent and satisfaction, contrast discovery with diversity and learn about marketplace and multi-stakeholder recommenders. Rishabh also introduces us into the creator economy at ShareChat.

In episode number 11 of Recsperts we meet Flavian Vasile who is a Principal Scientist at Criteo AI Lab. We dive into the specifics of personalized advertising and talk about click versus conversion optimization. Flavian also walks us through alternative recommender modelling approaches like economic and generative recommendations.

In episode ten of Recsperts we discuss the application of recommender systems to the human resources domain for matching people with jobs. I talk to David Graus, the Data Science Chapter Lead at Randstad which provides HR services to clients worldwide. David shares how recommender systems can support human recruiters by proposing the right candidates for vacancies. We also learn more about the biases that can play a role in that process and how to address them.

In episode nine of Recsperts we introduce RecPack which is the new recommender package for Python for easy, consistent and extensible experimentation and benchmarking. I talk to Lien Michiels and Robin Verachtert who are both industrial PhD students at the University of Antwerp. They also share how they provide modularized personalization for customers in the news and ecommerce sector at Froomle. In adition, we learn more about their research on filter bubbles as well as recommender model degradation and retraining.