Files
minstrel/internal/recommendation/score.go
T
bvandeusenandClaude Opus 5.5 fcf5b7e5a8 docs: wanted, dbtest, recommendation and playlists comments say what the code does (#6277)
The same rewrite as d191a282, for four packages whose comments carried the
most history: tuning stories, quotes from the operator, single-install
measurements in library_scale.go, and 'used to' accounts of bugs. The
constraint each story protected stays as a present-tense reason (why tier 3
is reduced not removed, why the randomised arms take an order seed, why the
seed artist is eligible in Songs-like). Comments naming system_cron.go and
config.RecommendationConfig, neither of which holds what they claimed, now
name the scheduler and internal/recsettings.

Checked: non-comment tokens identical to HEAD; gofmt clean.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
2026-10-23 12:19:15 -04:00

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// Package recommendation implements the weighted-shuffle scoring engine.
// The Score function is pure and takes an injectable RNG so
// tests can pin jitter to deterministic values.
package recommendation
import (
"time"
)
// ScoringInputs are the per-track facts the score function consumes.
// ContextualMatchScore is in [0, 1] — max similarity between the user's
// current session vector and any non-seed contextual_like row for this
// track. Set by LoadCandidates after a bulk fetch.
// SimilarityScore is in [0, 1]; 0 when no signal (random fill).
type ScoringInputs struct {
IsGeneralLiked bool
LastPlayedAt *time.Time // nil = never played
PlayCount int // total play_events
SkipCount int // play_events with was_skipped=true
ContextualMatchScore float64 // [0, 1]; 0 when no signal
SimilarityScore float64 // [0, 1]; 0 when no signal (random fill)
// TasteMatchScore is the candidate's fit to the user's learned taste
// profile, in [-1, +1]: positive draws a track toward the
// user's taste, negative reflects passive avoidance, 0 when there's no
// profile signal (cold start / artist+tags absent from the profile).
TasteMatchScore float64
// ContextAffinityScore is the candidate artist's time-of-day/weekday
// affinity for the CURRENT context, in [-1, +1]: positive when the
// artist's plays concentrate in the current daypart × weekday-type cell
// more than the user's baseline, negative when under-represented, 0 when
// there's no history (cold-start neutral).
ContextAffinityScore float64
}
// ScoringWeights are the tunable knobs. The shipped values and any tuned ones
// live in internal/recsettings, which hands them in per request.
type ScoringWeights struct {
BaseWeight float64
LikeBoost float64
RecencyWeight float64
SkipPenalty float64
JitterMagnitude float64
ContextWeight float64
SimilarityWeight float64
TasteWeight float64
ContextTimeWeight float64
}
// Score computes the weighted-shuffle score:
//
// score = base
// + (is_general_liked ? LikeBoost : 0)
// + recency_decay * RecencyWeight
// - skip_ratio * SkipPenalty
// + contextual_match_score * ContextWeight
// + similarity_score * SimilarityWeight
// + taste_match_score * TasteWeight
// + context_affinity_score * ContextTimeWeight
// + small_random_jitter
//
// Higher score = more likely to surface. rng is a function returning a
// uniform sample in [0,1) — pass math/rand.Float64 in production, a fixed
// value in tests.
func Score(in ScoringInputs, w ScoringWeights, now time.Time, rng func() float64) float64 {
s := w.BaseWeight
if in.IsGeneralLiked {
s += w.LikeBoost
}
s += recencyDecay(in.LastPlayedAt, now) * w.RecencyWeight
s -= skipRatio(in.PlayCount, in.SkipCount) * w.SkipPenalty
s += in.ContextualMatchScore * w.ContextWeight
s += in.SimilarityScore * w.SimilarityWeight
s += in.TasteMatchScore * w.TasteWeight
s += in.ContextAffinityScore * w.ContextTimeWeight
s += (rng()*2 - 1) * w.JitterMagnitude
return s
}
// recencyDecay returns a value in [0, 1]:
// - never played → 1.0 (cold-start tracks compete favorably with stale ones).
// - age < 30 days → linear ramp age_days / 30.
// - age ≥ 30 days → 1.0 (capped).
//
// Negative ages (clock skew) clamp to 0 to avoid math weirdness.
func recencyDecay(lastPlayed *time.Time, now time.Time) float64 {
if lastPlayed == nil {
return 1.0
}
age := now.Sub(*lastPlayed)
days := age.Hours() / 24
if days < 0 {
return 0.0
}
if days >= 30 {
return 1.0
}
return days / 30.0
}
// skipRatio returns skips/plays in [0, 1]; never-played tracks return 0
// rather than dividing by zero, so they aren't penalized.
func skipRatio(plays, skips int) float64 {
if plays == 0 {
return 0.0
}
return float64(skips) / float64(plays)
}