#12: From User Intent to Multi-Stakeholder Recommenders and Creator Economy with Rishabh Mehrotra
In episode number 12 of Recsperts we meet Rishabh Mehrotra, the Director of Machine Learning at ShareChat and former Staff Research Scientist & Area Tech Lead at Spotify. We discuss user need, intent and satisfaction, contrast discovery with diversity and learn about marketplace and multi-stakeholder recommenders. Rishabh also introduces us into the creator economy at ShareChat.
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:
Links from the Episode:
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:
- Mehrotra et al. (2017): Auditing Search Engines for Differential Satisfaction Across Demographics
- Mehrotra et al. (2018): Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommender Systems
- Mehrotra et al. (2019): Jointly Leveraging Intent and Interaction Signals to Predict User Satisfaction with Slate Recommendations
- Anderson et al. (2020): Algorithmic Effects on the Diversity of Consumption on Spotify
- Mehrotra et al. (2020): Bandit based Optimization of Multiple Objectives on a Music Streaming Platform
- Hansen et al. (2021): Shifting Consumption towards Diverse Content on Music Streaming Platforms
- Mehrotra (2021): Algorithmic Balancing of Familiarity, Similarity & Discovery in Music Recommendations
- Jeunen et al. (2022): Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders
General Links:
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- Send me your comments, questions and suggestions to marcel@recsperts.com
- Podcast Website: https://www.recsperts.com/
