The Fifth Elephant 2017

On data engineering and application of ML in diverse domains

Adapting Bandit Algorithms to optimise user experience at Practo Consult

Submitted by Santosh GSK (@santoshgadde) on Sunday, 30 April 2017

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Technical level

Intermediate

Section

Crisp talk for data engineering track

Status

Confirmed & Scheduled

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Total votes:  +30

Abstract

The art of trading between exploiting the best arm versus exploring for further knowledge of other arms has long been studied as Bandit Algorithms in various fields of clinical trials, designing financial portfolios, etc. Recently, in website optimization, these algorithms have been used for optimizing click through rates and performing A/B testing. However, these algorithms has the potential to be applied at several other contexts where we need to optimize for reward by exploring possible arms.

In this talk, I would be presenting an approach based on Contextual Multi-armed Bandit algorithms for achieving a better tradeoff between user’s expectation of faster replies and doctor’s burnout rate on a QnA platform like Practo Consult.

Background:

Practo’s Consult platform helps users get their health queries clarified by professional qualified doctors. As we value both users and doctors as our customers, we optimize for enhancing their experience while using Consult. This would entail good quality and faster replies for user’s health queries, whereas doctors expect good quality questions and number of assignments to be correlating with their answering capacity. This is an interesting problem because optimizing for one would compromise the other. For example, if we assign all questions to only the high performing doctors, the remaining non-performing doctors cannot undo their behavior as they won’t get enough questions. Whereas, if we balance the assignment of questions among doctors, it won’t be optimizing for faster replies. Ideally, we want to achieve a tradeoff between the two.

Outline

  1. The dynamics of a QnA platform like Practo Consult
  2. Introducing Multi-armed Bandit algorithm
  3. Adapting a version of Bandit algorithm called Contextual Multi-armed Bandit to enhance the experience of users and doctors.

Speaker bio

Santosh GSK is working as a Senior Data Scientist at Practo. He has 5 years of industry experience in Data Science and 3 years as a ML Researcher with half a dozen publications in leading conferences. He is currently working on building data-driven solutions to improve both patient and doctor experience at Practo. Prior to that, he was working as a Data Scientist at Housing.com, where he worked on lead prediction and property price prediction models.

Links

Slides

https://drive.google.com/open?id=0B_FveDU9pdasdjhpVDZpMXRKNFk

Preview video

https://www.youtube.com/watch?v=G6Rh2w-pftk&feature=youtu.be

Comments

  • 1
    Sandhya Ramesh (@sandhyaramesh) Reviewer a year ago

    Hey Santosh, could you upload some slides so that we can evaluate the content? Thanks!

    • 1
      Santosh GSK (@santoshgadde) Proposer a year ago

      Hey Sandhya, I have uploaded the slides. Please acknowledge.

  • 1
    Abhishek Balaji (@booleanbalaji) Reviewer a year ago

    Hi Santhosh,

    Thank you for uploading the slides. We need a few more details to evaluate this talk. Please upload a 2 minute video detailing what you intend to cover in this talk, the key takeaways from the talk and how your approach would benefit other engineers and data scientists.

  • 1
    Santosh GSK (@santoshgadde) Proposer a year ago

    Hey Abhishek, I have attached the preview video as requested. Please acknowledge.

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