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 this episode of Recsperts we talk to Flavian Vasile about the work of his team at Criteo AI Lab on personalized advertising. We learn about the different stakeholders like advertisers, publishers, and users and the role of recommender systems in this marketplace environment. We learn more about the pros and cons of click versus conversion optimization and transition to econ(omic) reco(mmendations), a new approach to model the effect of a recommendations system on the users' decision making process. Economic theory plays an important role for this conceptual shift towards better recommender systems.
In addition, we discuss generative recommenders as an approach to directly translate a user’s preference model into a textual and/or visual product recommendation. This can be used to spark product innovation and to potentially generate what users really want. Besides that, it also allows to provide recommendations from the existing item corpus.
In the end, we catch up on additional real-world challenges like two-tower models and diversity in recommendations.
Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
- (02:37) - Introduction Flavian Vasile
- (06:46) - Personalized Advertising at Criteo
- (18:29) - Moving from Click to Conversion optimization
- (23:04) - Econ(omic) Reco(mmendations)
- (41:56) - Generative Recommender Systems
- (01:04:03) - Additional Real-World Challenges in RecSys
- (01:08:00) - Final Remarks
Links from the Episode:
- Flavian Vasile on LinkedIn
- Flavian Vasile on Twitter
- Modern Recommendation for Advanced Practitioners - Part I (2019)
- Modern Recommendation for Advanced Practitioners - Part II (2019)
- CONSEQUENCES+REVEAL Workshop at RecSys 2022: Causality, Counterfactuals, Sequential Decision-Making & Reinforcement Learning for Recommender Systems
- Heymann et al. (2022): Welfare-Optimized Recommender Systems
- Samaran et al. (2021): What Users Want? WARHOL: A Generative Model for Recommendation
- Bonner et al (2018): Causal Embeddings for Recommendation
- Vasile et al. (2016): Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation