5 Commits

Include renames

Author	SHA1	Message	Date
bvandeusenandClaude Opus 4.8	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>	2026-07-14 09:31:43 -04:00
bvandeusenandClaude Opus 4.8	aff346c731	feat(taste): phase 2a — apply the taste profile via a TasteMatch scoring term (#796) ... 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>	2026-06-11 21:29:42 -04:00
bvandeusen	bb3f911761	feat(recommendation): extend Score with SimilarityScore + SimilarityWeight	2026-04-29 08:01:53 -04:00
bvandeusen	49871ba06d	feat(recommendation): extend Score with ContextualMatchScore + ContextWeight	2026-04-27 20:31:54 -04:00
bvandeusen	546234187f	feat(recommendation): add pure Score function with recency + skip + jitter ... 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.	2026-04-27 07:38:07 -04:00