The Fifth Elephant
The Fifth Elephant

The Fifth Elephant 2019

Gathering of 1000+ practitioners from the data ecosystem

Tickets
  • Select Tickets
  • Payment
  • invoice
  • Attendee details

Total ₹0

Cancellation and refund policy

Memberships can be cancelled within 1 hour of purchase

Workshop tickets can be cancelled or transferred upto 24 hours prior to the workshop.

For further queries, please write to us at support@hasgeek.com or call us at +91 7676 33 2020.

All submissions
This submission has been added to the schedule

Powered by VideoKen

Ayush mittal

@ayushmi

Feed Generation @ShareChat

Submitted Jun 24, 2019

Session type: Short talk of 20 mins Session type: Short talk of 20 mins

ShareChat is India’s largest vernacular social network platform built to enable next generation of India’s internet users. ShareChat is available in 14 vernacular languages. At ShareChat our data is fresh, with most users coming online for first time, our primary goal is to server most relevant content to the users at appropriate time. In this talk we will discuss the new challenges these first time internet user present. We will motivate the feed generation problem and give a walkthrough of Feed Generation algorithm at ShareChat.

Outline

  1. Introduction to ShareChat
  2. Understanding next billion users
  3. Feed Generation Problem at ShareChat
  4. Building a relevance model
  5. Feed Generation Architecture at ShareChat
  6. Data Challenges: Challenges in designing feed generation for ShareChat and unique insights that ShareChat’s data presents.

Speaker bio

Ayush is currently a lead data scientist at ShareChat. He designs algorithms for content-relevance and feed-generation. Ayush comes with past experience of working on a varied set of data science problems in different domains including Healthcare, Fin-Tech, Life Sciences and Manufacturing domains.

Slides

https://docs.google.com/presentation/d/1q9wcHYiquoKeEI1DkL9P6GZMS4lmwTP8bM3Z259hz2M/edit?usp=sharing

All submissions

Comments

Login to leave a comment

  • Zainab Bawa

    @zainabbawa Editor & Promoter

    Thanks for making the slides public, Ayush.

    Much of the slides after the intro are about the theory and comparison of deep learning in recommender systems. This makes the talk more a theoretical exposition to deep learning for recommender systems rather than a talk about the feed generation problem at Sharechat. Reduce this portion of the presentation.

    Here are some comments for which we need your responses:

    1. How (and why) do the deep learning algortihms underlying Youtube and Google recommender systems compare with Sharechat's context and product offering?
    2. It is unclear what is the problem at ShareChat for which you need to use Deep Learning and recommender systems? Explain the problem and the context in detail?
    3. What other approaches and solutions did you consider when solving the feed generation problem before narrowing down on this approach?
    4. What is the one innovation that you consider as a big win for the solution you came up with?
    5. How is the situation different before you implemented this solution vis-a-vis after you implemented this solution?

    Some of the slides are incomplete. These have to be completed by Wednesday, for a full review.

    Posted 5 years ago
Hybrid access (members only)

Hosted by

The Fifth Elephant
The Fifth Elephant
Jump starting better data engineering and AI futures