Milestone #160 Opt 3b. Adds device class as a third context axis on top
of the #1531 time-of-day/weekday affinity: on the radio path, a candidate
is boosted when its artist concentrates in the current (daypart × weekday
× device) cell. Client-sent (client_id is opaque; no UA stored), so it's
captured going forward and applies to radio only (daily mixes are
cron-built with no device → stay device-agnostic).
Server:
- Migration 0048: play_events.device_class text NULL (no CHECK; normalized
in Go — one whitelist entry per new client class, not a migration).
- events.go: eventRequest.device_class + normalizeDeviceClass (whitelist →
mobile/web/…, else "other", empty → NULL); threaded through both
RecordPlayStartedWithSource and RecordOfflinePlay into InsertPlayEvent.
- ListArtistContextPlayCountsForUser gains a current-device param; the cell
FILTER adds AND ($2='' OR device_class=$2) — '' reproduces the #1531
time-only behaviour exactly (used by mixes). SessionVector.DeviceClass
carries it; the radio handler derives the current device from the user's
latest play (GetLatestPlayDeviceClassForUser) — request-free proxy.
- No new tuning knob: device narrows the existing ContextAffinityScore
(reuses context_time_weight).
Clients:
- web: play_started sends device_class 'web'.
- android: play_started + offline replay send 'mobile' (EventsWire +
PlayOfflinePayload + MutationReplayer + PlayEventsReporter).
Test: LoadContextAffinity device-narrowing integration test (mobile vs web
artist separation; device-agnostic parity).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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
The profile built in phase 1 now changes what gets surfaced. Adds a TasteMatch
term to the weighted-shuffle score so candidates are re-ranked by their fit to
the user's learned taste (positive draws toward it; negative reflects passive
avoidance; 0 at cold start).
- recommendation/score.go: ScoringInputs.TasteMatchScore ([-1,+1]) +
ScoringWeights.TasteWeight + the term in Score.
- recommendation/taste.go: LoadTasteProfile reads the taste_profile_* tables;
TasteProfile.Match blends the candidate's artist weight (0.7) and avg genre-tag
weight (0.3), each tanh-squashed by a fixed scale so one outlier artist can't
compress the rest. Unknown artist/tags and empty profiles → 0 (neutral).
- candidates.go: both candidate loaders set TasteMatchScore per candidate, so
every Score caller (system playlists incl. You-might-like, radio) becomes
taste-aware automatically.
- weights: systemMixWeights.TasteWeight = 1.5 (daily mixes are the primary
taste surface); config.RecommendationConfig gains taste_weight (default 1.0,
lighter — radio is seed-directed) wired into the radio handler.
- tests: pure (Match curve incl. saturation/clamp/empty-neutral, Score term
add+subtract) + DB round-trip (seed taste rows → Match positive). All green
vs real Postgres; existing playlist/radio tests unaffected (empty profile →
zero taste effect).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Wire LoadCandidatesFromSimilarity as the primary candidate loader with
an ?exclude= query param for comma-separated UUID filtering; fall back
to LoadCandidates on error. Thread SimilarityWeight into ScoringWeights
and update testHandlers recCfg accordingly.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Wire contextual scoring end-to-end: add ContextWeight to RecommendationConfig
(struct + Default()), fetch the user's current session vector in handleRadio
via loadCurrentSessionVector (cold-start returns Seed sentinel), and pass it
as the 6th arg to LoadCandidates so ContextualMatchScore flows into Shuffle.
Validates seed_track + optional limit (default cfg.Recommendation.RadioSize,
clamped to RadioSizeMax). Calls recommendation.LoadCandidates +
recommendation.Shuffle. Prepends seed to result. Server seeds a
math/rand source at startup; handlers package threads that as a
func() float64 so tests inject deterministic RNGs.
Mount + server.New gain a RecommendationConfig parameter.
M6 stub. Validates seed_track param (400 on missing/blank/bad UUID),
looks up the track (404 on miss), returns RadioResponse with a single
TrackRef. M4 will replace the body with similarity-driven candidate
pool + scoring; the request/response shape is final.
bvandeusen