Sep 23–26, 2019

Recommendation systems using deep learning (Day 2)

Bargava Subramanian (Binaize), Amit Kapoor (narrativeVIZ)
Location: 1A 03

Who is this presentation for?

  • You're a data scientist, product manager, data analyst, or software engineer




In the digital world, recommendation systems play a significant role—both for the users and for the company. For users, it opens up a new world of options that were tough to find. For companies, it helps drive user engagement and satisfaction, directly impacting the bottom line. If you’ve shopped on an ecommerce site or watched a movie on an on-demand video platform, you’ve seen options like, “People who viewed this product also viewed…” or “Products similar to this one.…” These are the results from recommendation systems (recsys).

Amit Kapoor and Bargava Subramanian walk you through the different paradigms of recommendation systems and introduced you to deep learning-based approaches. You’ll leave with enough practical hands-on knowledge to build, select, deploy, and maintain a recommendation system.



  • Why build recommendation systems? Scope and evolution of recsys
  • Challenges: Accuracy, ranking, relevance, novelty, serendipity, and diversity
  • Paradigms in recommendations: Content based, collaborative filtering, knowledge based, hybrid, and ensemble
  • Key concepts in recsys: Explicit versus implicit feedback, user-item matrix, domain signals (location, time, context, and social)
  • Why use deep learning for recsys? Traditional versus deep learning approaches
  • Primer on deep learning with examples and use cases

Content based

  • Introduction to the case: Product recommendation
  • Environment setup for hands-on session
  • Feature extraction using deep learning: Embeddings for heterogeneous data
  • Exercise: Recommending items using similarity measures

Collaborative filtering

  • Overview of traditional collaborative filtering for recsys
  • Primer on deep learning approaches: Deep matrix factorization and autoencoders
  • Exercise: Recommending items using collaborative filtering

Learning to rank

  • Why learning to rank? Prediction versus ranking
  • Rank learning approaches: Pointwise, pairwise, and listwise
  • Deep learning approach to combine prediction and ranking
  • Exercise: Recommending items using learning to rank

Hybrid recommender

  • Introduction to the case: Text recommendation
  • Combining content-based and collaborative filtering
  • Primer on wide and deep learning for recommender systems
  • Exercise: Recommending items using hybrid recommender

Time and context

  • Adding temporal component: Window and decay based
  • Adding context context through group recommendations
  • Dynamic and sequential modeling using recurrent neural networks (RNNs)
  • Exercise: Recommending items using RNN recommender

Deployment and monitoring

  • Deploying the recommendation system models
  • Measuring improvements from recommendation system
  • Improving the models based on the feedback from production
  • Architecture design for recsys: Offline, nearline, and Online

Evaluation, challenges, and way forward

  • A/B testing for recommendation systems
  • Challenges in recsys: Building explanations, model debugging, scaling out and up, fairness, accountability, and trust
  • Bias in recsys: Training data, UI → Algorithm → UI, private
  • When not to use deep learning for recsys
  • Recap, next steps, and learning resources

Prerequisite knowledge

  • Experience with programming
  • Familiarity with Python data stack
  • Working knowledge of machine learning principles
  • A basic understanding of machine learning problems (e.g., regression and classification)
  • General knowledge of linear algebra and calculus (useful but not required)

What you'll learn

  • Get a thorough introduction to recommendation systems and paradigms across domains
  • Gain an end-to-end view of deep learning-based recommendation and learning-to-rank systems
  • Understand practical considerations and guidelines for building and deploying recommendation systems for their own problems
Photo of Bargava Subramanian

Bargava Subramanian


Bargava Subramanian is a cofounder and deep learning engineer at Binaize in Bangalore, India. He has 15 years’ experience delivering business analytics and machine learning solutions to B2B companies. He mentors organizations in their data science journey. He holds a master’s degree from the University of Maryland, College Park. He’s an ardent NBA fan.

Photo of Amit Kapoor

Amit Kapoor


Amit Kapoor is a data storyteller at narrativeViz, where he uses storytelling and data visualization as tools for improving communication, persuasion, and leadership through workshops and trainings conducted for corporations, nonprofits, colleges, and individuals. Interested in learning and teaching the craft of telling visual stories with data, Amit also teaches storytelling with data for executive courses as a guest faculty member at IIM Bangalore and IIM Ahmedabad. Amit’s background is in strategy consulting, using data-driven stories to drive change across organizations and businesses. Previously, he gained more than 12 years of management consulting experience with A.T. Kearney in India, Booz & Company in Europe, and startups in Bangalore. Amit holds a BTech in mechanical engineering from IIT, Delhi, and a PGDM (MBA) from IIM, Ahmedabad.

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