Design Spotify's homepage

Asked in: recsys system design at music / video / content companies.

The L4 candidate proposes one ranker. The L6 candidate decomposes the surface into shelves with distinct objectives and a meta-layer that decides what gets shown.

What’s distinctive about a homepage

It’s not one recommendation problem. A homepage like Spotify’s has many shelves (Made For You, Recently Played, New Releases, Daily Mix, Discover Weekly, Podcasts, etc.), each with a distinct purpose:

Some surface what the user already loves (low novelty, high engagement).
Some surface new things (high novelty, lower per-item engagement, long-term retention).
Some are personalized; some are editorial.

The hardest design decision is how the shelves interact: which appear, in what order, with what content.

What an L5 answer sounds like

“Treat the homepage as two layers:

Layer 1: per-shelf rankers. Each shelf has its own retrieval and ranking pipeline tuned to its purpose:

Made For You / Daily Mix: retrieve from user’s listening history embedding space, rank by predicted listen-completion + repeat-listen probability.

New Releases: retrieve from new content (last 7-30 days) matched to user taste; rank balancing freshness with personalization.

Discover Weekly: retrieve far from user’s history (exploration); rank by predicted appeal calibrated for novelty.

Recently Played: simple recency.

Each shelf is its own multi-stage retrieve-then-rank pipeline.

Layer 2: shelf composition. A meta-ranker decides which shelves appear on the homepage and in what order, per user, per session. Inputs: user features, time of day, device, recent shelf engagement. Output: an ordered list of shelves to show.

Eval: each shelf has its own metrics (CTR, listen-time, completion). The composition layer optimizes a session-level metric (total session time, return rate, satisfaction). A/B test composition changes against a fixed baseline shelf order.”

This is L5. Two layers, each shelf as its own ranker, meta-layer for composition.

What an L6 answer adds

“…practical things:

Diversity is critical. Without explicit diversity, the homepage collapses to similar items everywhere. Explicit diversification at three levels: within-shelf (don’t show 10 songs from the same artist), across-shelf (different shelves should serve different intents), and over-time (don’t show the same item every day).

Editorial vs algorithmic mix. Pure algorithmic recsys is fragile (filter bubbles, missed cultural moments). Editorial shelves (Today’s Top Hits, regional charts, curated playlists) anchor the experience. The meta-ranker decides the mix.

Cold-start handling. New users see fewer personalized shelves and more popularity-based / editorial. Transition to personalized as listen history accumulates. New items get a freshness boost in candidate generation; their long-term ranking depends on accumulated engagement.

Multi-task ranking per shelf. Don’t predict ‘click probability’ alone. Predict listen-time, completion, save, share, return-next-day. Combine with weights tuned via offline counterfactual eval and confirmed in A/B.

Latency budget: homepage loads need ~200ms p99. Cache aggressively per user (re-compute every few hours, not per request). For real-time signals (just-finished song), update only the affected shelves.

Long-term metrics gate launches. Short-term engagement is easy to optimize and easy to cannibalize the long-term experience. Track session-quality, return rate, churn proxies; require long-window A/B (4-8 weeks) for any composition change.”

Tells that get you a strong-hire vote

You decompose into shelves with distinct objectives.
You add a meta-ranker for composition.
You bring up editorial / algorithmic mix.
You discuss diversity at multiple levels.
You insist on long-window A/B tests for composition changes.

Tells that get you down-leveled

One ranker for the whole homepage.
No mention of diversity or editorial content.
No cold-start handling.
Optimizing only short-term clicks.

Common follow-up

“How would you order the shelves for a brand-new user?”

The L6 answer:

“For a new user with no history, the meta-ranker has no personal signal, so it falls back on three things: (1) editorial defaults (top playlists, popular new releases), (2) onboarding signal if any (genre selections from sign-up flow), (3) demographic and contextual proxies (region, device, time of day). The order should make engagement easy: hits and recognized content first, exploration second, personalized last (since the personalization will be weak). After a few sessions, transition to the personalized rank.”