#32: RecSys in the Delivery Industry at Wolt with Sasha Fedintsev
In episode 32 of Recsperts, I’m joined by my colleague Sasha Fedintsev, Staff Applied Scientist at Wolt (DoorDash), working across personalization and ads, to unpack the realities of building large-scale recommender systems in food, grocery, and retail delivery. Together, we discuss the specifics of personalization in the delivery domain, and the models and ideas that power Wolt’s recommender system across 30+ markets - where theory quickly meets messy, high-stakes practice.
We explore what makes this domain fundamentally different from traditional e-commerce: strong locality constraints, real-time context, and a heavy skew toward repurchasing behavior. Sasha explains how these factors break many textbook approaches - like standard collaborative filtering - and require creative adaptations such as clustering strategies and multi-stage ranking systems optimized for latency, all while respecting locality constraints.
We also discuss the evolution of recommendation approaches over time - from classical collaborative filtering with ALS, to Neural Collaborative Filtering with BPR, and ultimately to transformer-based models for user sequence modeling and next-purchase prediction powering today’s venue ranking systems.
We also touch on practical challenges such as evaluation in real-world systems, including A/B testing pitfalls and biases in logged data, as well as the complexity introduced by multi-surface experiences like discovery pages, vertical lists, and search. Beyond venues, we discuss why item-level recommendation is an order of magnitude harder - due to scale, context dependence, and availability constraints - and what this implies for future system design.
Throughout the episode, Sasha provides a candid view on the evolving role of a Staff Applied Scientist - bridging research and production, setting scientific standards, and driving cross-team impact.
Enjoy this enriching episode of RECSPERTS – Recommender Systems Experts.
Don’t forget to follow the podcast and please leave a review.
We explore what makes this domain fundamentally different from traditional e-commerce: strong locality constraints, real-time context, and a heavy skew toward repurchasing behavior. Sasha explains how these factors break many textbook approaches - like standard collaborative filtering - and require creative adaptations such as clustering strategies and multi-stage ranking systems optimized for latency, all while respecting locality constraints.
We also discuss the evolution of recommendation approaches over time - from classical collaborative filtering with ALS, to Neural Collaborative Filtering with BPR, and ultimately to transformer-based models for user sequence modeling and next-purchase prediction powering today’s venue ranking systems.
We also touch on practical challenges such as evaluation in real-world systems, including A/B testing pitfalls and biases in logged data, as well as the complexity introduced by multi-surface experiences like discovery pages, vertical lists, and search. Beyond venues, we discuss why item-level recommendation is an order of magnitude harder - due to scale, context dependence, and availability constraints - and what this implies for future system design.
Throughout the episode, Sasha provides a candid view on the evolving role of a Staff Applied Scientist - bridging research and production, setting scientific standards, and driving cross-team impact.
Enjoy this enriching episode of RECSPERTS – Recommender Systems Experts.
Don’t forget to follow the podcast and please leave a review.
- (00:00) - Introduction
- (05:10) - About Sasha Fedintsev
- (15:26) - The Role of a Staff Applied Scientist
- (25:50) - Challenges and Specifics of the Delivery Industry
- (47:24) - Ranking and Recommendation Problems at Wolt
- (51:31) - NCF with BPR for Wolt's First DNN Recommendation Model
- (01:16:43) - User Sequence Transformers for Next Purchase Prediction
- (01:26:51) - Explore vs. Exploit or New vs. Recurring Purchases
- (01:31:29) - Ads Personalization at Wolt
- (01:36:16) - Further Challenges in RecSys
- (01:37:58) - A Final Note on Radical Longevity
- (01:46:30) - Closing Remarks
Links from the Episode:
- Alexander "Sasha" Fedintsev on LinkedIn
- Alexander on X
- Wolt
- Alexander Fedintsev at Wolt Tech Talks: Restaurant discovery with Wolt: Deep Neural Networks to power recommendations
- H3 Geospatial Indexing System
- Recommenders Repository
- Tanja Reilly: The Staff Engineer's Path
- Will Larson: Staff Engineer: Leadership beyond the management track
- Coupon collector's problem
- Alexander Fedintsev (2026): Longevity Bottlenecks: Part I — Dementia
Papers:
- Rendle et al. (2009): BPR: Bayesian personalized ranking from implicit feedback
- He et al. (2017): Neural Collaborative Filtering
- Dacrema et al. (2019): Are we really making much progress? A worrying analysis of recent neural recommendation approaches
- Rendle et al (2020): Neural Collaborative Filtering vs. Matrix Factorization Revisited
- Hu et al. (2008): Collaborative Filtering for Implicit Feedback Datasets
- Grbovic et al. (2015): E-commerce in Your Inbox: Product Recommendations at Scale
- Quadrana et al. (2018): Sequence-Aware Recommender Systems
- Su et al. (2024): Long-Term Value of Exploration: Measurements, Findings and Algorithms
- Tran et al. (2024): Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation
- Lichtenberg et al. (2024): Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending
General Links:
- Follow me on LinkedIn
- Follow me on X
- Send me your comments, questions and suggestions to marcel.kurovski@gmail.com
- Recsperts Website
