011b4d9a9c912357ed59c9119c45599ad07ed0d9
5
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 bvandeusen
|
481f906059 |
feat(metrics): publish margin of error on every delta — #2495, #2524
The metrics card had one volume threshold doing two jobs. recMetricsLowVolume = 20 is a DISPLAY floor — below that a skip rate is anecdote — but the card then presented deltas as though it were also a DECISION floor. Those differ by an order of magnitude: detecting the ~13pp differences that matter needs ~133 plays per arm for 80% power at a=0.05. So Discover's taste-matched (59 plays) and random-unheard (70) both rendered as full-confidence rows with a bold delta beside them, and that comparison sits at p ~ 0.06. The card said "signal"; the arithmetic said "maybe". It produced a recommendation the data didn't support, and any reader with the same numbers would have made the same call. Deltas now carry a 95% margin of error and a `distinguishable` flag, computed server-side so both clients read the same arithmetic instead of each re-deriving it. Skip rate is a two-proportion difference; completion is Welch, which needs a variance — hence completion_sqsum in the query. It is the sum of squares rather than stddev_samp on purpose: raw source rows are merged into surface families in Go, and sums of squares combine across groups exactly whereas standard deviations cannot. recMetricsLowVolume is untouched. "Too thin to show" and "too thin to act on" are different questions. Web renders an indistinguishable delta as dimmed and prefixed "≈", with the range on hover and a legend explaining the glyph. Colour is withheld unless the delta clears its margin — colouring noise red is what made the old card misleading. Breakdown rows go through the same path; those are the thinnest samples on screen and where the old card misled most. Also fixes the admin trends view, which had the same problem worse: its "Latest skip"/"Latest completion" columns are one WEEK while the adjacent Plays column is the whole window. I misread exactly that and briefly concluded Deep cuts was the worst surface, from ~17 plays in a single week — over 180 days it is one of the best. Headers now name their period and the skip cell carries that week's play count. #2524: resolveArtist now recognises a duplicate-MBID unique violation as the expected condition it is, matching resolveAlbum. Two rows mapping to one MusicBrainz artist is a merge candidate, not a fault; without the branch it logged a generic warning plus a Postgres ERROR line on every scan, which teaches an operator to ignore database errors. |
||
 bvandeusenandClaude Fable 5
|
9ad4343c76 |
feat(tuning): weekly trend view — per-surface series + knob-turn markers
The verify half of the tune→verify loop (#1251), on the same admin Tuning page as the knobs: - RecommendationWeeklyTrends: weekly per-source outcomes aggregated across all users (the knobs are global, so judging a turn needs global outcomes — rows carry rates only, no track/user identity), with a taste-hit count per bucket: plays whose track's artist has a positive weight in the player's current taste profile. That's the "cheap recompute" reading — retroactive over the whole window, at the cost of profile drift. - GET /api/admin/recommendation-trends?weeks=N (default 12, cap 52): per-family weekly series (skip rate, sample-weighted completion, taste-hit rate) plus the tuning-audit markers inside the window. - Web: sparkline table under the tuning cards — skip rate per week on a shared axis with dashed ticks at knob turns, latest-week columns, window taste-hit rate, low-volume rows dimmed as anecdote, and a plain-text list of the window's tuning changes. Also fixes the revive unused-parameter lint on the tuning GET handler that failed CI run 1903 on the previous commit. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01TsF3cNoKrqCYsU78cXC8U6 |
||
|
bvandeusenandClaude Fable 5
|
fb4431207d |
feat(recommendation): For You exploration attribution — taste vs fresh picks
Milestone #127 step 2 (#1249). For You deliberately blends two populations — a head of top-scored taste picks and a tail sampled from deeper ranking (the freshness injection) — but the metrics judged it as one blob, so its skip rate couldn't distinguish "the taste engine is missing" from "the freshness tax is too high". That number decides the exploration share before we tune it. - Migration 0038: nullable pick_kind ('taste'|'fresh') on both playlist_tracks (stamped at snapshot build) and play_events (frozen at play-ingestion — the snapshot rebuilds daily, so attribution cannot be reconstructed at read time). - Builder: pickHeadAndTail marks head=taste / tail=fresh; the small-pool fallback is all taste (top-N-by-score IS the taste mechanism). Other variants persist NULL. - Ingestion: for_you plays (live + offline replay) look the track up in the user's current snapshot; not found → unattributed, never guessed. - Metrics: For You's row gains a breakdown (taste / fresh / earlier unattributed plays), parent row stays the sum; web card renders the sub-rows indented with the same baseline deltas + low-data dimming. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01TsF3cNoKrqCYsU78cXC8U6 |
||
|
bvandeusenandClaude Fable 5
|
60533073ad |
feat(metrics): bucketed surface families + manual-plays baseline (#1248, milestone 127)
The recommendation metrics table was observable but not actionable: raw source strings (album:<uuid> one-offs) drowned the stable surfaces, and manual plays were excluded so skip rates had no control group. - SQL: include NULL-source rows (the baseline) and carry completion_n so family merges can weight avg_completion correctly. - Handler buckets raw sources into stable families (radio:<uuid> → Radio, album:/artist: → direct plays, etc.) grouped by surface intent: go-to / discovery / direct — each band judged against its job, since discovery mixes are expected to skip hotter. Families under 20 plays are flagged low-confidence, not hidden. - Settings card renders the baseline row and per-surface deltas in percentage points vs baseline (worse-than-baseline deltas in danger color), intent hint copy per group, low-data rows dimmed. - Pure-unit test for the bucketing/merge; DB test updated to the new contract (baseline included, radio:<uuid> collapse). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01TsF3cNoKrqCYsU78cXC8U6 |
||
|
bvandeusenandClaude Opus 4.8
|
1a7515e6ea |
feat(taste): phase 4 — recommendation observability (#796)
Per-source play outcomes so the operator can see whether each recommendation
surface is landing and tune the now-operator-tunable taste weights.
Server:
- query RecommendationSourceMetricsForUser: groups the user's play_events by
source (system-playlist surface), reporting plays / skips / avg completion
over a window; NULL-source (library/radio) plays excluded.
- GET /api/me/recommendation-metrics?days=30 (default 30, capped 365) →
{window_days, sources:[{source, plays, skips, skip_rate, avg_completion}]}.
- handler test: 401 unauth; per-source aggregation + NULL-source exclusion +
skip_rate / avg_completion math.
Web:
- lib/api/metrics.ts: query + friendly source labels.
- settings page gains a "Recommendation metrics" card (table of surface / plays
/ skip rate / avg completion), with loading/error/empty states.
- settings tests mock the new query (manual subscribe-store, hoisting-safe).
Note: You-might-like plays aren't source-tagged (it's a Home row, not a system
playlist), so this covers For-You / Discover / the mixes. Tagging YML plays
would be a client follow-up.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
|