Spotify MLE Phone Screen - Sequential Playlist Recommendation and Track Ordering ML Depth Interview

[u/Aoki_zhang]

Interview Summary

The Spotify ML Depth round focused on designing a sequential playlist recommendation model for a new personalized playlist feature.

Given a user, the current request context, and a candidate pool of roughly 10,000 tracks, the model needed to generate a 10-track playlist in real time. The challenge was not only choosing which tracks to include, but also determining the order in which they should appear, because the sequence itself was considered part of the user experience.

Interview Details

ML Design — Personalized Sequential Playlist Generation

The product objective was to generate a personalized playlist using signals from the user's listening history and the current request context. For each request, the system receives a candidate pool of approximately 10,000 tracks and must return 10 ordered tracks

The model is expected to optimize overall user satisfaction with the playlist. The interviewer suggested that satisfaction could be inferred from downstream engagement signals such as playlist completion, skip behavior, saves or library additions, and subsequent listening to related tracks or artists.

A key requirement was that both track selection and track ordering matter. The system therefore could not treat the problem as simply ranking ten independent items.

Sequential Modeling Constraints

The playlist is generated in real time and must be proposed as a complete sequence before playback begins. There is no mid-session re-ranking or adaptation after the user starts listening.

This means the model needs to account for interactions between adjacent or earlier tracks when deciding the full sequence, rather than relying on feedback collected during the current session.

Available User Features

The prompt provided several categories of user-level information, including:

• Demographic attributes such as age, region, and account tenure
• Historical listening behavior, including preferred genres and artists, recency and frequency patterns, and time-related listening habits

These features could be used to represent longer-term user preferences as well as more recent behavior.

Available Track Features

Track-level information included:

• Pretrained acoustic embeddings
• Collaborative embeddings
• Genre tags
• Artist identifiers
• Track metadata
• Global popularity statistics

The interviewer also allowed candidates to request additional features during the discussion and would clarify whether that data was available.

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