The Fifth Elephant 2019

The eighth edition of India's best data conference


Improving product discovery via Hierarchical Recommendations!

Submitted by Neha Kumari (@neha-kumari) on Friday, 5 April 2019

Preview video

Session type: Lecture Session type: Full talk of 40 mins


A recommendation engine’s primary goal is to surface personalised & relevant content to the user, content which satisfies explicit intent as well as serendipitous content that would otherwise be invisible. E-commerce categories such as Lifestyle, have a lot of flux, the trends last for a short time window and have their demand distributed across an extensive selection. In such cases, recommending product collections can be a better idea instead of individual products.

In this talk I will be talking about the recommendation system at Flipkart, our journey towards recommending collection and how it improved the product discovery and helped in solving cold start problem. I will also cover the relevance algorithm used which is a hybrid of collaborative and content-based recommendation, and how this is achieved at scale, where we have catalogue consisting of ~400M products and this will be ever growing in this ecommerce world.

The Why ?
1. To solve cold start problem and to improve product discovery which aids to coverage
2. Increasing basket size and improving engagement post purchase
3. Cross category discovery and search result augmentation


  • Introduction to recommendation system at Flipkart
  • Problem in hand
  • Our journey towards recommending collections
  • How hierarchical product taxonomies can be leveraged to solve cold-start problem and improving product discovery
  • Relevance algorithm@scale
  • Captivating findings and results

Speaker bio

Neha is a software developer with Recommendation team at Flipkart. She has worked on building scalable systems for product recommendations and personalisation. In the past she has also worked on Natural Language Processing. She is interested in building robust data processing pipelines at scale, and applying Machine Learning to solve challenging problems . She has graduated from IIT BHU. While not working on official projects, she involves herself in technical writing and blogging. She also contributes to the open source world by answering technical questions.



Preview video


  • Anwesha Sarkar (@anweshaalt) Reviewer 2 months ago

    Thank you for submitting the proposal. Submit your slides and preview video by 20th April (latest) it helps us to close the review process.

  • Zainab Bawa (@zainabbawa) Reviewer a month ago

    The slides are not publicly accessible, Neha. Change permission settings. Also, upload the two-min preview video explaining what this talk is about and why participants should attend?

    • Neha Kumari (@neha-kumari) Proposer a month ago

      I have updated the slides to be publicily accessible. Will update the video in a while.

      • Neha Kumari (@neha-kumari) Proposer a month ago

        I have added the video link as well.

  • Zainab Bawa (@zainabbawa) Reviewer a month ago

    We have evaluated the slides and have the following feedback:

    1. The problem has to be stated as a general problem statement rather than a Flipkart problem statement. This is because companies which don’t have Flipkart’s problems should be able to identify with the problem per se, rather than the company. For example, is the problem that there is a long-tail problem in the discovery of content about lifestyle products?
    2. The slides dive straight into the solution without explaining why this approach was considered for the solution? Which other approaches were considered? How was the evaluation done before finalizing that the problem will be solved in this manner?
    3. The key takeaways are not obvious because there are no details – such as before-after scenarios, data from implementation.
    4. Also, the slides don’t show what has changed after implementing this solution, and the challenges of using this solution at scale?

    The slides need to be much more detailed, incorporating the above feedback. Upload revised slides by or before 21 May in order to close the decision on your proposal.

  • Anwesha Sarkar (@anweshaalt) Reviewer 25 days ago

    Thank you for the submission we will get back to you soon.

  • Abhishek Balaji (@booleanbalaji) Reviewer 6 days ago

    Rehearsal scheduled

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