bvandeusen
65dd132b3d
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>
58 lines
2.0 KiB
Go
58 lines
2.0 KiB
Go
package recommendation
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import (
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"testing"
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"time"
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)
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func TestContextAffinity(t *testing.T) {
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const baseline = 0.4 // 40% of the user's plays fall in the current cell
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const k = contextAffinityShrinkage
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// Heavy history, over-represented in the current cell → positive.
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if a := contextAffinity(80, 100, baseline, k); a <= 0 {
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t.Errorf("over-represented artist affinity = %.3f, want positive", a)
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}
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// Heavy history, under-represented → negative.
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if a := contextAffinity(10, 100, baseline, k); a >= 0 {
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t.Errorf("under-represented artist affinity = %.3f, want negative", a)
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}
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// A sparse artist (1/1) shrinks toward the baseline, so its affinity is
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// smaller than a heavily-played artist with the same raw cell-share.
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sparse := contextAffinity(1, 1, baseline, k)
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heavy := contextAffinity(100, 100, baseline, k)
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if sparse >= heavy {
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t.Errorf("sparse (%.3f) should shrink below heavy (%.3f)", sparse, heavy)
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}
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// No plays → neutral.
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if a := contextAffinity(0, 0, baseline, k); a != 0 {
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t.Errorf("no plays affinity = %.3f, want 0", a)
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}
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// Result stays within [-1, 1].
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for _, tc := range [][2]float64{{100, 100}, {0, 100}, {50, 50}} {
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a := contextAffinity(tc[0], tc[1], baseline, k)
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if a < -1 || a > 1 {
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t.Errorf("affinity out of [-1,1]: %.3f", a)
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}
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}
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}
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func TestScore_ContextTermAddsAndSubtracts(t *testing.T) {
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now := time.Now()
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zeroJitter := func() float64 { return 0.5 } // (0.5*2-1)=0 with any magnitude
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w := ScoringWeights{ContextTimeWeight: 2.0} // all other weights 0
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pos := Score(ScoringInputs{ContextAffinityScore: 1.0}, w, now, zeroJitter)
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if !almostEq(pos, 2.0) {
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t.Errorf("positive context affinity: Score = %.3f, want 2.0", pos)
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}
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neg := Score(ScoringInputs{ContextAffinityScore: -1.0}, w, now, zeroJitter)
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if !almostEq(neg, -2.0) {
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t.Errorf("negative context affinity: Score = %.3f, want -2.0 (demotes)", neg)
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}
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off := Score(ScoringInputs{ContextAffinityScore: 1.0}, ScoringWeights{}, now, zeroJitter)
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if !almostEq(off, 0.0) {
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t.Errorf("ContextTimeWeight 0: Score = %.3f, want 0 (no effect)", off)
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}
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}
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