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>
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>
Implements spec §6 weighted-shuffle scoring without the
contextual_match_score term (sub-plan #3 adds it). Pure Go, no DB
dependency; injectable RNG for deterministic tests. Coverage 100%
on score.go via the boundary tests.
bvandeusen