Spotify ML Question - Design a Recommendation System (Full mock interview)

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In this ML mock interview, a FanDuel machine learning engineer designs a machine learning system for personalizing music recommendations on Spotify, highlighting data preparations, metrics, model choices, production considerations, and room for improvement.
Chapters (Powered by ChapterMe) -
00:00 - Intro
01:52 - Data engagement, clicks, users, metadata
06:32 - Building models in batches or real-time
07:05 - Data pipeline design and features overview
09:19 - Data normalization for Spotify users clicks
12:13 - Data cleanup and age group predictions
16:11 - Content filtering and collaborative filtering for recommendation
20:07 - Choosing model, collaborative filtering, pitfalls
21:34 - Importance of training, validation, and production
22:15 - Cloud computing simplifies model testing
23:50 - Metrics and model success
24:44 - Engagement and churn metrics determine models performance
26:10 - Key insights for recommending artists
30:04 - ML interview analysis key takeaways
31:18 - Game plan, production, and detail improvement
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[@tryexponent]

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[@Basant5911]

Use two tower network user and items, turn it to floating point embeddings. normalize the continuous features like age then concatenate the embedding to user and item embeddings. Then use softmax approximations to find reduce time complexity.

[@adityahpatel]

This is very well thought of by Sid. One question - why are there no diagrams to draw on whiteboard for this? Usually, all interviews ask you to draw a system design flow chart/diagram of some type.

[@sohaibarshid5642]

Great video with very nice insights. Learned a lot from this interview. Keep up the good work!!!

[@claude7222]

would’ve liked to see him talking through evaluation of model during training

[@sophiophile]

Just starting the video, but evaluating negative recommendations based on simply not clicking I feel can lead to issues with recommenders. I think that you need to distinguish between soft negatives like not clicking, and some sort of hard negative (like user feedback, or clicking away very quickly). From a stakeholder perspective, getting people to expand the number of different artists/songs they are listening to is beneficial, and you can expect users to need to see a recommendation a few times before it is given a negative label and used for your latent embedding.

[@sophiophile]

Seems like she was trying to lead him down the path of segmenting engagement metrics to tie it back to refining the model to meet stakeholder priorities/business outcomes. If you are performing quite well keeping existing users engaged with your recommendations, incrememtal further improvements to that are not going to impact revenue as much as preventing churn- where you need to focus on new and low-engagement users. Instead, try to build separate recommenders for those groups- so you dont need to degrade the performance of your power-user recommendation enginge in search of preventing churn.

[@Spectraevil]

Decent video but it mainly caters to junior ML engineers.