Personalized Recommendations at Scale
4 weeks US$ 400
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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.

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Rishabh Mehrotra
Director of Machine Learning at ShareChat
US$ 400
Course Duration
4 weeks
Start Date
July 11
Registration By
July 10
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Connect with experts through live sessions and office hours
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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.

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The course
Week 1
Multi-stage composition of large scale recommender
  • Aspects of recommendations & personalization
  • Deep candidate generation techniques: negative sampling, contrastive learning
  • System design example of few large scale recommenders
Implement 1-3 candidate generators, from simple recall-based CGs to noise contrastive estimator-based CGs. Conduct recall-based evaluation of the different approaches.
    Week 2
    ML approaches for ranking and recommendations
    • Classical approaches of recommendations: embedding, matrix factorization, factorization machines
    • Multi-task recommenders: predicting multiple facets of user engagement
    • [Optional deep dive] Sequential recommenders: sequence aware approaches, in-session personalization
    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.
      Week 3
      Online learning from user interactions
      • Learning user representations: leveraging user interactions to learn user representations
      • Learning user intents: methods to understand & predict user intent
      • Learning from feedback: online methods of feedback incorporation
      Implement two user representation techniques and compare their performance on downstream recommendation tasks.
        Week 4
        Measurement: Offline, online & counterfactual evaluation
        • 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.
          This course is for...

          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

          Course experience

          Live Sessions with Experts

          Top industry leaders teach you everything you need in only 4 weeks

          Interactive Learning

          Real-world projects put your learning into immediate action

          Professional Communities

          Grow your network by learning with an intimate cohort of peers from top companies
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