May 16, 2024

RecSys at Spotify // Sanket Gupta // #232

50 minutes

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Sanket works as a Senior Machine Learning Engineer at Spotify working on building end-to-end audio recommender systems. Models built by his team are used across Spotify in many different products including Discover Weekly and Autoplay.

MLOps podcast #232 with Sanket Gupta, Senior Machine Learning Engineer at Spotify //

RecSys at Spotify.

A big thank you to LatticeFlow for sponsoring this episode! LatticeFlow - https://latticeflow.ai/

// Abstract

LLMs with foundational embeddings have changed the way we approach AI today. Instead of re-training models from scratch end-to-end, we instead rely on fine-tuning existing foundation models to perform transfer learning.

Is there a similar approach we can take with recommender systems?

In this episode, we can talk about:

a) how Spotify builds and maintains large-scale recommender systems,

b) how foundational user and item embeddings can enable transfer learning across multiple products,

c) how we evaluate this system

d) MLOps challenges with these systems

// Bio

Sanket works as a Senior Machine Learning Engineer on a team at Spotify building production-grade recommender systems. Models built by my team are being used in Autoplay, Daily Mix, Discover Weekly, etc.

Currently, my passion is how to build systems to understand user taste - how do we balance long-term and short-term understanding of users to enable a great personalized experience.

// MLOps Jobs board

https://mlops.pallet.xyz/jobs

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

Website: https://sanketgupta.substack.com/

Our paper on this topic "Generalized User Representations for Transfer Learning": https://arxiv.org/abs/2403.00584

Sanket's blogs on Medium in the past: https://medium.com/@sanket107

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Connect with Sanket on LinkedIn: www.linkedin.com/in/sanketgupta107

Timestamps:

[00:00] Sanket's preferred coffee

[00:37] Takeaways

[02:30] RecSys are RAGs

[06:22] Evaluating RecSys parallel to RAGs

[07:13] Music RecSys Optimization

[09:46] Dealing with cold start problems

[12:18] Quantity of models in the recommender systems

[13:09] Radio models

[16:24] Evaluation system

[20:25] Infrastructure support

[21:25] Transfer learning

[23:53] Vector database features

[25:31] Listening History Balance

[26:35 - 28:06] LatticeFlow Ad

[28:07] The beauty of embeddings

[30:13] Shift to real-time recommendation

[34:05] Vector Database Architecture Options

[35:30] Embeddings drive personalized

[40:16] Feature Stores vs Vector Databases

[42:33] Spotify product integration strategy

[45:38] Staying up to date with new features

[47:53] Speed vs Relevance metrics

[49:40] Wrap up

...more

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By Demetrios

4.9

2020 ratings