bvandeusenandClaude Opus 5.5
fcf5b7e5a8
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>
102 lines
2.9 KiB
Go
102 lines
2.9 KiB
Go
package recommendation
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// SimilarityWeights balances the per-axis contribution to the weighted Jaccard
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// score. v1 hardcodes the defaults — operators cannot tune via YAML. If
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// telemetry justifies it, expose under recommendation.similarity.* later.
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type SimilarityWeights struct {
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TagsWeight float64
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ArtistsWeight float64
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}
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// DefaultSimilarityWeights is the shipped axis balance.
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// Tags carry more signal than artists because a session's "vibe" tracks
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// genre more directly than artist identity (a session can mix artists
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// within a genre but rarely mixes genres).
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var DefaultSimilarityWeights = SimilarityWeights{
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TagsWeight: 0.7,
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ArtistsWeight: 0.3,
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}
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// Similarity returns weighted-Jaccard similarity in [0, 1] between two
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// session vectors. Returns 0 if either input is Seed=true (low-confidence
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// vectors don't contribute to scoring).
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func Similarity(a, b SessionVector, w SimilarityWeights) float64 {
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if a.Seed || b.Seed {
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return 0.0
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}
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tagJ := setJaccardKeys(a.Tags, b.Tags)
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artistJ := setJaccardSlice(a.Artists, b.Artists)
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return tagJ*w.TagsWeight + artistJ*w.ArtistsWeight
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}
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// setJaccardKeys collapses two map keysets to sets and returns
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// |A ∩ B| / |A ∪ B|. Both empty → 0 (not NaN).
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func setJaccardKeys(a, b map[string]int) float64 {
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if len(a) == 0 && len(b) == 0 {
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return 0.0
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}
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intersect := 0
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for k := range a {
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if _, ok := b[k]; ok {
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intersect++
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}
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}
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union := len(a) + len(b) - intersect
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if union == 0 {
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return 0.0
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}
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return float64(intersect) / float64(union)
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}
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// setJaccardSlice deduplicates each input slice into a set and returns
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// |A ∩ B| / |A ∪ B|. Both empty → 0 (not NaN).
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func setJaccardSlice(a, b []string) float64 {
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if len(a) == 0 && len(b) == 0 {
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return 0.0
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}
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aset := make(map[string]struct{}, len(a))
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for _, x := range a {
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aset[x] = struct{}{}
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}
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bset := make(map[string]struct{}, len(b))
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for _, x := range b {
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bset[x] = struct{}{}
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}
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intersect := 0
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for k := range aset {
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if _, ok := bset[k]; ok {
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intersect++
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}
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}
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union := len(aset) + len(bset) - intersect
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if union == 0 {
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return 0.0
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}
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return float64(intersect) / float64(union)
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}
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// ContextualMatchScore returns the maximum Similarity between the current
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// session vector and any non-seed entry in likes. Returns 0 when:
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// - current.Seed is true (no meaningful current context)
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// - likes is empty after filtering out Seed=true entries
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//
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// The "max" semantics means a single strong contextual match dominates
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// over many weak ones — we want to surface the track because it was liked
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// in *some* matching context, not because it was vaguely-liked in many.
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func ContextualMatchScore(current SessionVector, likes []SessionVector, w SimilarityWeights) float64 {
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if current.Seed {
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return 0.0
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}
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best := 0.0
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for _, l := range likes {
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if l.Seed {
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continue
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}
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s := Similarity(current, l, w)
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if s > best {
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best = s
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}
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}
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return best
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}
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