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Commits
| Author | SHA1 | Message | Date | |
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  bvandeusenandClaude Opus 5
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f367eeaa9d |
fix(recommendation): Songs-like gets its own profile so it stops wandering
test-web / test (push) Successful in 1m7s
test-go / test (push) Successful in 1m31s
test-go / integration (push) Failing after 4m21s
release / Build signed APK (releases and dev) (push) Successful in 5m11s
release / Build + push container image (push) Successful in 1m52s
release / Verify release artifacts (tag releases only) (push) Skipped
Operator, 2026-09-10: "when I play it I'm expecting to get a consistent
sound and style from the experience... I was getting a seeming wide variety
of music from each one when I was hoping to stay in a certain neighborhood."
Songs-like shared the `daily_mix` weight profile with For-You, and that
sharing WAS the bug. The two surfaces want opposite things: For-You answers
"what will they enjoy today" and is supposed to roam; Songs-like answers
"what sounds like THIS". Under one profile the broad answer wins.
The arithmetic, from the shared weights:
unrelated track, liked, not played recently → 1.0 + 2.0 + 1.0 = 4.0
PERFECT similarity match, not liked → 1.0 + 1.5 = 2.5
Liking something outranked sounding like the seed, because LikeBoost (2.0)
exceeded SimilarityWeight's whole range (1.5) and TasteWeight (1.5, and
seed-INDEPENDENT) matched it outright. Under the new profile the same pair
scores 5.00 vs 2.00.
Two levers, because either alone leaves the other's failure intact:
POOL. Songs-like now takes its own CandidateSourceLimits. The default gave
~29% of candidates a sim_score of literally zero — `taste_overlap` and
`random_fill` are both `0.0::float8` in recommendation.sql, seed-independent
by construction. Same total pool size; composition shifts to arms that
measure distance from the seed, LBSimilar doubled.
WEIGHTS. A third profile beside radio and daily_mix, DB-backed and live per
rule 25, with the property that similarity's range exceeds the combined
range of every seed-independent differentiator — so a closer match cannot
be beaten on likes, freshness and taste alone, while tracks within ~0.39
similarity of each other still get ordered by what the user likes.
Rule 131 changed the pool design mid-way and for the better. Zeroing the
two seed-independent arms was the first instinct and is exactly the
vanish-or-nothing shape that rule forbids: a seed with thin ListenBrainz
coverage would yield a short mix or none. They are the tier-3 FLOOR — cut
hard, never removed — and the weights keep them at the bottom of the
ranking rather than out of the pool. "A few tracks further from the seed
than we'd like" beats "no playlist".
Caught while wiring it: switching only pickTopN's final Score would have
been nearly INERT. scoreAndSortCandidates does the selection sort, and the
caller caps and truncates in that order — so the playlist would still have
been chosen by daily_mix and merely relabelled with songs_like numbers. It
now takes the profile as a parameter, and each surface passes its own.
Also corrects the daily_mix card's blurb, which claimed Songs-like as one
of its surfaces and no longer is.
Guards pin behaviour rather than the numbers, since numbers get retuned:
that similarity beats an unrelated liked track, that daily_mix still
DOESN'T (or the split buys nothing), that the tier-3 floor is non-zero,
and that the UI card shows its own values rather than falling back. Each
falsified against its named regression first.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SQ31KQpYbStyK5y58UmPLH
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bvandeusen
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96abb48086 |
test(tuning): query the window/last-week headers as column headers — #2495
test-web / test (push) Failing after 33s
getByText(/Plays/) matched my own new caption as well as the header, since the caption explains which columns cover the window. Query by columnheader role instead, which is what the assertion actually means. Also reordered the caption: prepending the clarification turned it into a run-on that opened mid-explanation before saying what the chart was. |
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bvandeusen
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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. |
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bvandeusenandClaude Opus 5
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ca4832e620 |
feat(discover): Discover tuning card on the admin lab — #2377 (web admin)
test-web / test (push) Successful in 35s
Rule #25/#27: the two knobs slice 6 added server-side are now touchable — taste-tag weight and snooze length, with deviation dots, save, and reset, matching the existing profile/taste cards. Copy states what each knob does AND what it doesn't: the tag-weight hint says 0 turns the term off and that an untagged candidate is never penalised, and the snooze hint says it records no opinion about the artist and never feeds the taste profile. Those are the two properties most likely to be assumed backwards by whoever turns these next. Also fixed a latent fragility the new card exposed rather than caused: all three reset buttons had the accessible name "Reset to defaults", so the existing test picked the LAST one and assumed that meant taste. Adding a card below it would have silently retargeted that assertion at the wrong scope. Each reset button now names its scope — better for screen readers too, since three identical buttons on one page is a real a11y defect — and the test selects by name instead of position. The page's test fixture needed the new `discover` key in both `snapshot` and `shipped`: the `as TuningSnapshot` cast means a missing field is not a compile error, it's every test on the page throwing inside fillForm. Noted that in the fixture so the next scope doesn't rediscover it. Includes a test that a weight of 0 is actually SENT rather than dropped as falsy — the off switch is the one value a truthiness bug would eat. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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f0c08e7326 |
feat(taste): mood taste facet — #1534
Milestone #160 Opt 2b (mood half of the era+mood option). A fourth taste facet alongside artists + genre tags + eras: signed weights over canonical mood buckets (melancholic / energetic / chill / …) derived from a track's enriched folksonomy tags (#1490). - internal/mood: shared vocabulary — Of(tags) maps folksonomy tags to canonical mood buckets (synonyms collapse). Imported by both the taste builder and the scorer so a track's mood is derived identically. - Migration 0047: taste_profile_moods table + taste_tuning.mood_scale (DEFAULT 0.5). - Build side (internal/taste): Config.MoodScale ([0,1] damper, mirrors EraScale); accumulate folds each play/like's mood buckets at base*MoodScale; persist atomic-replaces the mood rows. - Scorer (internal/recommendation): TasteProfile gains a mood term (own tanh scale + additive 0.12 share, so it never weakens the existing signal when a track has no mood tags). Match now takes the candidate's mood buckets; loaded per candidate (ListTrackTagsForTracks → mood.Of) in the primary similarity loader only — the near-whole-library fallback pool passes nil (mood → 0) to avoid a full-library tag scan. - Tuning lab: mood_scale threaded through recsettings + admin API + web card ("Mood weight" row) + Go/web tests. Coverage is partial (grows with tag enrichment; richer once Last.fm is keyed), so mood is a supplement — neutral for tracks with no mood tags. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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65dd132b3d |
feat(taste): time-of-day / weekday context conditioning — #1531
Milestone #160 Opt 3 (temporal half). A new additive scoring term that boosts a candidate when its artist's play history concentrates in the CURRENT daypart × weekday-type cell, in the user's local timezone. - Migration 0046: recommendation_weight_profiles.context_time_weight (per-profile scoring weight, DEFAULT 1.0). - Query ListArtistContextPlayCountsForUser: per-artist completed-play counts split by the current cell (daypart night[22,5)/morning[5,12)/ afternoon[12,17)/evening[17,22) × weekday-vs-weekend) via started_at AT TIME ZONE users.timezone; 365-day window, skips excluded. - internal/recommendation/context.go: LoadContextAffinity computes each artist's shrunk cell-share minus the user's baseline share, clamped to [-1,1]; sparse artists shrink toward baseline (pseudo-count 5), unknown artists → 0 (cold-start neutral). - Score() gains context_affinity_score · ContextTimeWeight; both candidate loaders set it per candidate. - Tuning lab: ContextTimeWeight threaded through recsettings + admin API + web card ("Time-of-day weight" row) + Go/web tests. Shipped 1.0 both profiles (uniform start, re-bakeable). Device-class axis deferred to #1551 (needs a client_id → device-class mapping that doesn't exist yet). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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40384cc05e |
feat(taste): era/decade taste facet — #1530
Milestone #160 Opt 2 (era half). A third taste facet alongside artists + genre tags: signed weights over decade buckets ("1990s") derived from albums.release_date, rebuilt daily and scored into the taste match. - Migration 0045: taste_profile_eras table (mirrors taste_profile_tags) + taste_tuning.era_scale column (DEFAULT 0.5). - Build side (internal/taste): Config.EraScale ([0,1] damper, mirrors EnrichedTagScale), accumulate folds each play/like's decade at base*EraScale, persist atomic-replaces the era rows. - Scorer (internal/recommendation): TasteProfile gains an era term (own tanh scale + additive 0.15 share so it never weakens the existing artist/tag signal when a track is undated); candidate queries return album release_date; decadeOf mirrors the builder helper. - Tuning lab: era_scale threaded through recsettings + admin API + web card (auto-renders the new row) + Go/web tests. Mood facet deferred to #1534 (partial enrichment coverage + needs candidate-side enriched-tag loading). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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2b3be8311a |
feat(tuning): expose EnrichedTagScale in the tuning lab (#1520)
Promote the enriched-tag weight (#1490) from a taste.Config default into the DB-backed tuning lab so operators can dial how much folksonomy tags count vs raw ID3 genre (rule #25). - Migration 0044: taste_tuning.enriched_tag_scale (DEFAULT 0.5, backfills the existing row). - recsettings: TasteTuning gains the field; seeded/read/updated through reconcile + persistTaste; applyTastePatch validates it to [0,1] (generic non-half-life clamp) and diffTaste audits it; TasteConfig maps it into the profile build. - API: tasteTuningResp exposes enriched_tag_scale. - Web tuning card: a data-driven "Enriched tag weight" knob (0 = genre only). Tests: recsettings persist+range, web fixture field. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Fable 5
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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 |
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bvandeusenandClaude Fable 5
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0d0a8f46b1 |
feat(tuning): scoring weights → DB-backed admin tuning lab
The recommendation scoring knobs move out of YAML (radio profile) and out of the systemMixWeights hard-code (daily_mix profile) into DB-backed settings with live effect (#1250) — the defaults-discovery lab per decision #1247: the operator turns knobs to find good values, which then get baked back into shipped defaults; end users and other operators should never need the card. - Migration 0040: recommendation_weight_profiles (radio / daily_mix, 8 weight columns), taste_tuning singleton (engagement half-life + completion-curve points), recommendation_tuning_audit (one row per change with a {field, old, new} diff — the trend view's markers, #1251). - internal/recsettings: boot reconcile seeds shipped defaults without clobbering tuned rows (coverart SettingsService pattern), validates patches (bounds, curve ordering), writes audit rows, and pushes daily_mix weights + taste config into package playlists. No-op patches write no audit row. - playlists gains SetSystemMixWeights / SetTasteConfig swap points under a RWMutex — no signature threading through the producers; the scheduler's taste rebuild reads the pushed config. - Radio reads its weight profile from the service per request; the 8 weight fields leave config.RecommendationConfig (YAML keeps only RecentlyPlayedHours / RadioSize / RadioSizeMax). - Admin API: GET/PATCH/reset under /api/admin/recommendation-tuning, echoing current + shipped values. - Web: new admin Tuning tab — two weight profiles side by side, taste card, per-scope save (changed fields only) + reset, deviation dots against shipped defaults. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01TsF3cNoKrqCYsU78cXC8U6 |