refactor: split recommendation engine into subpackage

internal/anime/recommendations/scoring.go

@@ -0,0 +1,117 @@
+package recommendations
+
+import (
+	"mal/integrations/jikan"
+	"math"
+	"time"
+)
+
+func profileSearchRankWeight(rank int) float64 {
+	return math.Max(0.35, 1-(float64(rank)*0.08))
+}
+
+func rankedCandidateRetrievalScore(collaborativeScore float64, profileSearchScore float64) float64 {
+	return (math.Log1p(collaborativeScore) * collaborativeWeight) +
+		(profileSearchScore * profileSearchWeight)
+}
+
+func hasTasteMetadata(anime jikan.Anime) bool {
+	return len(anime.Genres) > 0 ||
+		len(anime.Themes) > 0 ||
+		len(anime.Studios) > 0 ||
+		len(anime.Demographics) > 0
+}
+
+func scoreRecommendationCandidate(
+	now time.Time,
+	profile userTasteProfile,
+	candidate jikan.Anime,
+	collaborativeScore float64,
+	profileSearchScore float64,
+) recommendationCandidate {
+	genreMatches, genreScore := weightedEntityMatch(profile.genres, candidate.Genres)
+	themeMatches, themeScore := weightedEntityMatch(profile.themes, candidate.Themes)
+	studioMatches, studioScore := weightedEntityMatch(profile.studios, candidate.Studios)
+	demographicMatches, demographicScore := weightedEntityMatch(profile.demographics, candidate.Demographics)
+
+	score := rankedCandidateRetrievalScore(collaborativeScore, profileSearchScore)
+	score += genreScore * genreMatchWeight
+	score += themeScore * themeMatchWeight
+	score += studioScore * studioMatchWeight
+	score += demographicScore * demographicMatchWeight
+	score += recommendationCandidateScoreAdjustments(now, profile, candidate)
+
+	return recommendationCandidate{
+		anime:              candidate,
+		score:              score,
+		genreMatches:       genreMatches,
+		themeMatches:       themeMatches,
+		studioMatches:      studioMatches,
+		demographicMatches: demographicMatches,
+	}
+}
+
+func recommendationCandidateScoreAdjustments(now time.Time, profile userTasteProfile, candidate jikan.Anime) float64 {
+	var score float64
+
+	if candidate.Score > 0 {
+		score += min(candidate.Score/10.0, 1.0)
+	}
+	if candidate.Popularity > 0 {
+		score += 1.0 / math.Log(float64(candidate.Popularity)+8)
+	}
+	if profile.prefersAiring && candidate.Airing {
+		score += 0.5
+	}
+	if profile.prefersRecent && isRecentCandidate(now, candidate.Year) {
+		score += 0.45
+	}
+	if isClassicCandidate(now, candidate.Year) {
+		score -= 0.2
+	}
+	if candidate.Status == "Not yet aired" {
+		score -= 0.35
+	}
+	if isFreshRelease(now, candidate.Aired.From) {
+		score += 0.3
+	}
+
+	return score
+}
+
+func isRecentCandidate(now time.Time, year int) bool {
+	return year > 0 && now.Year()-year <= 4
+}
+
+func isClassicCandidate(now time.Time, year int) bool {
+	return year > 0 && now.Year()-year > 15
+}
+
+func isFreshRelease(now time.Time, airedFrom string) bool {
+	if airedFrom == "" {
+		return false
+	}
+
+	airedAt, err := time.Parse(time.RFC3339, airedFrom)
+	if err != nil {
+		return false
+	}
+
+	return now.Sub(airedAt) <= freshReleaseWindow
+}
+
+func weightedEntityMatch(weights map[int]float64, entities []jikan.NamedEntity) (int, float64) {
+	var matches int
+	var score float64
+
+	for _, entity := range entities {
+		weight, ok := weights[entity.MalID]
+		if !ok {
+			continue
+		}
+		matches++
+		score += weight
+	}
+
+	return matches, score
+}