Personalized Recommendations at Scale
Surfacing relevant content from among millions of candidates to users in real-time is a challenging task addressed by recommender systems. Most modern-day recommender systems rely on the complex interplay between different components, each of which is powered by sophisticated machine learning algorithms. In this course, we provide a holistic overview of ML modeling choices that go into developing and deploying multi-stage recommenders capable of serving recommendations from hundreds of million content choices to multiple hundred million users. The course goes into algorithmic models that power the various stages of the recommender, including the candidate generator, core ranker, user representation learning modules and offline and online evaluation module. The course ends with case studies, lessons and practical considerations from deployed systems powering over 400 million users.
Course taught by expert instructors
Rishabh Mehrotra
Director of Machine Learning at ShareChat
Rishabh Mehrotra currently works as a Director of Machine Learning at ShareChat based in London. His current research focuses on machine learning for marketplaces, multi-objective modeling of recommenders, and the creator ecosystem. Prior to ShareChat, he was an Area Tech Lead and Staff Scientist/Engineer at Spotify where he led multiple ML projects from basic research to production across 400+ million users. Rishabh has a PhD in Machine Learning from UCL, and 50+ research papers and patents. Some of his recent work has been published at conferences including KDD, WWW, SIGIR, RecSys, and WSDM. He has co-taught a number of tutorials and summer school courses on the topics of learning from user interactions, marketplaces, and personalization.
The course
Learn and apply skills with real-world projects.
Industry practitioners tasked with developing and deploying a large scale recommender system
MLEs with prior machine learning experience looking into diving deep into large scale recommender systems
Some familiarity with basic machine learning concepts like model training, feature representations, labels
Ability to write Python and work with documented libraries
Try these prep courses first
- Learn
- Recommendation Problem Formulation
- Multi-stage Recommender System
- ML Approaches for Generating Recommendations
- Modern Recommender Systems
Implement 1-3 candidate generators, from simple recall-based CGs to noise contrastive estimator-based CGs. Conduct recall-based evaluation of the different approaches.
- Learn
- Recommendation Rankers
- Contextual Bandits for Recommendations
- Playlist Recommendation Model
Implement a multi-task recommender, predicting multiple user engagement signals, and combining various predictions to serve the top-k recommendations. Perform various ranking and user engagement-based evaluations.
- Learn
- Learning User Representations
- Topical Representation of Users
- Learning About Users
Implement two user representation techniques and compare their performance on downstream recommendation tasks.
- Learn
- Beyond AUC: Offline Evaluation Setup, Session Level Metrics, Implicit Signals Logged Data
- Nuances of Online Experimentation: AB Test Design, and Online Metrics
- Offline-online Correlation: Scaling
- Counterfactual Evaluation: Randomized Data Collection, Unbiased Estimators of Metrics
Using logged data of user interactions, implement a few session-based metrics, and identify the biases of the metric.
Real-world projects
Work on projects that bring your learning to life.
Made to be directly applicable in your work.
Live access to experts
Sessions and Q&As with our expert instructors, along with real-world projects.
Network & community
Core reviews a study groups. Share experiences and learn alongside a global network of professionals.
Support & accountability
We have a system in place to make sure you complete the course, and to help nudge you along the way.
Get reimbursed by your company
More than half of learners get their Courses and Memberships reimbursed by their company.
Hundreds of companies have dedicated L&D and education budgets that have covered the costs.
Course success stories
Learn together and share experiences with other industry professionals
This is my second course from CoRise and I am really happy with the number of new things I learned. The material was quite in-depth and the projects were rather challenging but quite fulfilling. A lot of material on recommenders can really only be found in research papers and the team at CoRise has managed to condense a lot of that into a 4-week course, which you could never get anywhere else.
It's been amazing to learn from an industry expert in RecSys himself. Rishabh and CoRise team structured the course in such a way that the salient details are covered really well. The pragmatic touch through projects was a cherry on top! I would definitely suggest anyone who has an interest in implementing to the deploying their recommender systems at scale to take this course!
Rishabh is an expert in recommendations and you can feel his passion for the field throughout the course. We covered some of the hottest aspects of recommendations nowadays in a very hands-on manner 🙂.
If you have some experience with Recommender Systems, you will find Personalized Recommendations at Scale taking you to the next level. The course introduces you to various concepts relevant to industrial systems and it will help you understand them through practical exercises at the end of each week. The community is constructive and you will find many experienced members helping you throughout the projects and make good connections while you learn and have fun.
Great course that was approachable enough for most practitioners while still getting deep into the weeds about state-of-the-art ongoings in recommendation.
