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Using Locations for Online-Behaviour Prediction with Sparse Data

Submitted by Nishant Oli (@nishantoli) on Tuesday, 30 April 2019


Preview video

Section: Crisp talk Technical level: Intermediate Session type: Lecture

Abstract

User behaviour varies with their current location, as a consequence their engagement with online media (say Ads) varies with where they are; Knowing the type of location can help us target the user better and recommend better.

After the advent of GPS technology, it is easy and inexpensive to get location information from devices, but the data is often sparse for an ad network as they only get a partial view of user data. We present a novel algorithm to classify locations using the GPS data along with anonymous ad-request data such that it performs optimally with sparse GPS signals with no supervised data at all.

Our results show that the algorithm has a better prediction accuracy compared to existing methods and correlates well with the user behaviours.

Outline

  1. Introduction / Context Setting
  2. Why the need to classify the locations
  3. Location as the key attribute for recommendations
  4. Data exploration and modeling.
  5. Deploying the model using Spark
  6. How users interact differently at different locations
  7. Visualization of results for Bangalore and Bay Area.
  8. Results and take home message

Speaker bio

Nishant Oli is a Data Scientist at InMobi where he works on user inferences and geo analytics. He graduated with a Master’s degree from IIIT Bangalore. Previously he worked at Siemens Corporate Research & Mazumdar Shaw Center of Translational Research in the field of Meta-Learning for computer vision and biomedical computer vision.

Links

Slides

https://www.slideshare.net/secret/iIq47wmdQHwtxm

Preview video

https://youtu.be/BZ3CMq_rQRU

Comments

  • Abhishek Balaji (@booleanbalaji) Reviewer 5 months ago

    Hello Nishant,

    Thank you for submitting a proposal. To proceed with evaluation, we need to see detailed slides and a preview video for your proposal. Your slides must cover the following:

    • 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 alternatives, and what metrics did you use for the evaluation?
    • Why did you pick the option that you did?
    • Explain how the situation was before the solution you picked/built and how it changed 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 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?
    • Is the tool free/open-source? If not, what can the audience takeaway from the talk?

    We need to see the updated slides on or before 21 May in order to close the decision on your proposal. If we do not receive an update by 21 May we’ll move the proposal for consideration at a future event.

  • Nishant Oli (@nishantoli) Proposer 5 months ago

    Hi Abhishek,

    I have updated the slides based on your comments, please let me know if there are any changes required.

    Best,
    Nishant

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