Anthill Inside 2019

On infrastructure for AI and ML: from managing training data to data storage, cloud strategy and costs of developing ML models

Propose a session

Non-Intent User Similarity for recommendation systems

Submitted by Gunjan Sharma (@gunjan-sharma) on Thursday, 25 April 2019

Section: Full talk Technical level: Intermediate

Abstract

In the world of Ad Business’s recommendation systems it is easier comparatively to recommend to user who have shown some intent. But what about the users who have not shown any intent? How do you target them? In this talk I will like to talk about a novel approach to use user similarity from supply data to work out significant recommendation for these users

Outline

Define the problem
Define non efficient solutions
Introduce the novel user similarity approach
Connect it to the original problem to show how it helps
Results
Future work

Speaker bio

Gunjan Sharma
Architect InMobi

Comments

  • Abhishek Balaji (@booleanbalaji) Reviewer 2 months ago

    Hi Gunjan,

    Thank you for submitting a proposal. For us to evaluate your proposal, we need to see detailed slides and a preview video. Your slides must take the following points into consideration:

    • Problem statement/context, which the audience can relate to and understand. The problem statement has to be a problem (based on this context) that can be generalized for all.
    • What were the tools/options available in the market to solve this problem? How did you evaluate these, and what metrics did you use for the evaluation? Why did you decide to build your own ML model?
    • Why did you pick the option that you did?
    • Explain how the situation was before the solution you picked/built and how was the fraud/ghosting after implementing the solution you picked and built? Show before-after scenario comparisons & metrics.
    • What compromises/trade-offs did you have to make in this process?
    • What are the privacy, regulatory and ethical considerations when building this solution?
    • What is the one takeaway that you want participants to go back with at the end of this talk? What is it that participants should learn/be cautious about when solving similar problems?

    As next steps, we’d need to see the detailed and/or updated slides by 21 May, in order to close the decision on your proposal. If we dont receive an update by 21 May, we’d have to move the proposal for consideration for a future conference.

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