Artwork

Content provided by Marcel Kurovski. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Marcel Kurovski or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Player FM - Podcast App
Go offline with the Player FM app!

#12: From User Intent to Multi-Stakeholder Recommenders and Creator Economy with Rishabh Mehrotra

2:05:03
 
Share
 

Manage episode 352836335 series 3288795
Content provided by Marcel Kurovski. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Marcel Kurovski or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this episode of Recsperts we talk to Rishabh Mehrotra, the Director of Machine Learning at ShareChat, about users and creators in multi-stakeholder recommender systems. We learn more about users intents and needs, which brings us to the important matter of user satisfaction (and dissatisfaction). To draw conclusions about user satisfaction we have to perceive real-time user interaction data conditioned on user intents. We learn that relevance does not imply satisfaction as well as that diversity and discovery are two very different concepts.

Rishabh takes us even further on his industry research journey where we also touch on relevance, fairness and satisfaction and how to balance them towards a fair marketplace. He introduces us into the creator economy of ShareChat. We discuss the post lifecycle of items as well as the right mixture of content and behavioral signals for generating recommendations that strike a balance between revenue and retention.

In the end, we also conclude our interview with the benefits of end-to-end ownership and accountability in industrial RecSys work and how it makes people independent and effective. We receive some advice for how to grow and strive in tough job market times.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.

Chapters:

  • (03:44) - Introduction Rishabh Mehrotra
  • (19:09) - Ubiquity of Recommender Systems
  • (23:32) - Moving from UCL to Spotify Research
  • (33:17) - Moving from Research to Engineering
  • (36:33) - Recommendations in a Marketplace
  • (46:24) - Discovery vs. Diversity and Specialists vs. Generalists
  • (55:24) - User Intent, Satisfaction and Relevant Recommendations
  • (01:09:48) - Estimation of Satisfaction vs. Dissatisfaction
  • (01:19:10) - RecSys Challenges at ShareChat
  • (01:27:58) - Post Lifecycle and Mixing Content with Behavioral Signals
  • (01:39:28) - Detect Fatigue and Contextual MABs for Ad Placement
  • (01:47:24) - Unblock Yourself and Upskill
  • (02:00:59) - RecSys Challenge 2023 by ShareChat
  • (02:02:36) - Farewell Remarks

Links from the Episode:

Papers:

General Links:

  continue reading

23 episodes

Artwork
iconShare
 
Manage episode 352836335 series 3288795
Content provided by Marcel Kurovski. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Marcel Kurovski or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this episode of Recsperts we talk to Rishabh Mehrotra, the Director of Machine Learning at ShareChat, about users and creators in multi-stakeholder recommender systems. We learn more about users intents and needs, which brings us to the important matter of user satisfaction (and dissatisfaction). To draw conclusions about user satisfaction we have to perceive real-time user interaction data conditioned on user intents. We learn that relevance does not imply satisfaction as well as that diversity and discovery are two very different concepts.

Rishabh takes us even further on his industry research journey where we also touch on relevance, fairness and satisfaction and how to balance them towards a fair marketplace. He introduces us into the creator economy of ShareChat. We discuss the post lifecycle of items as well as the right mixture of content and behavioral signals for generating recommendations that strike a balance between revenue and retention.

In the end, we also conclude our interview with the benefits of end-to-end ownership and accountability in industrial RecSys work and how it makes people independent and effective. We receive some advice for how to grow and strive in tough job market times.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.

Chapters:

  • (03:44) - Introduction Rishabh Mehrotra
  • (19:09) - Ubiquity of Recommender Systems
  • (23:32) - Moving from UCL to Spotify Research
  • (33:17) - Moving from Research to Engineering
  • (36:33) - Recommendations in a Marketplace
  • (46:24) - Discovery vs. Diversity and Specialists vs. Generalists
  • (55:24) - User Intent, Satisfaction and Relevant Recommendations
  • (01:09:48) - Estimation of Satisfaction vs. Dissatisfaction
  • (01:19:10) - RecSys Challenges at ShareChat
  • (01:27:58) - Post Lifecycle and Mixing Content with Behavioral Signals
  • (01:39:28) - Detect Fatigue and Contextual MABs for Ad Placement
  • (01:47:24) - Unblock Yourself and Upskill
  • (02:00:59) - RecSys Challenge 2023 by ShareChat
  • (02:02:36) - Farewell Remarks

Links from the Episode:

Papers:

General Links:

  continue reading

23 episodes

All episodes

×
 
Loading …

Welcome to Player FM!

Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.

 

Quick Reference Guide