Tweak SimilarSongs algorithm to prioritize the requested main artist

core/external_metadata.go

@@ -163,28 +163,37 @@ func (e *externalMetadata) SimilarSongs(ctx context.Context, id string, count in
 		return nil, ctx.Err()
 	}
 
-	artists := model.Artists{artist.Artist}
-	artists = append(artists, artist.SimilarArtists...)
-
 	weightedSongs := utils.NewWeightedRandomChooser()
-	for _, a := range artists {
+	addArtist := func(a model.Artist, weightedSongs *utils.WeightedChooser, count, artistWeight int) error {
 		if utils.IsCtxDone(ctx) {
 			log.Warn(ctx, "SimilarSongs call canceled", ctx.Err())
-			return nil, ctx.Err()
+			return ctx.Err()
 		}
 
 		topCount := utils.MaxInt(count, 20)
 		topSongs, err := e.getMatchingTopSongs(ctx, e.ag, &auxArtist{Name: a.Name, Artist: a}, topCount)
 		if err != nil {
 			log.Warn(ctx, "Error getting artist's top songs", "artist", a.Name, err)
-			continue
+			return nil
 		}
 
-		weight := topCount * 4
+		weight := topCount * (4 + artistWeight)
 		for _, mf := range topSongs {
 			weightedSongs.Put(mf, weight)
 			weight -= 4
 		}
+		return nil
+	}
+
+	err = addArtist(artist.Artist, weightedSongs, count, 10)
+	if err != nil {
+		return nil, err
+	}
+	for _, a := range artist.SimilarArtists {
+		err := addArtist(a, weightedSongs, count, 0)
+		if err != nil {
+			return nil, err
+		}
 	}
 
 	var similarSongs model.MediaFiles

utils/weighted_random_chooser.go

@@ -6,29 +6,29 @@ import (
 	"time"
 )
 
-type weightedChooser struct {
+type WeightedChooser struct {
 	entries     []interface{}
 	weights     []int
 	totalWeight int
 	rng         *rand.Rand
 }
 
-func NewWeightedRandomChooser() *weightedChooser {
+func NewWeightedRandomChooser() *WeightedChooser {
 	src := rand.NewSource(time.Now().UTC().UnixNano())
 
-	return &weightedChooser{
+	return &WeightedChooser{
 		rng: rand.New(src), // nolint:gosec
 	}
 }
 
-func (w *weightedChooser) Put(value interface{}, weight int) {
+func (w *WeightedChooser) Put(value interface{}, weight int) {
 	w.entries = append(w.entries, value)
 	w.weights = append(w.weights, weight)
 	w.totalWeight += weight
 }
 
 // GetAndRemove choose a random entry based on their weights, and removes it from the list
-func (w *weightedChooser) GetAndRemove() (interface{}, error) {
+func (w *WeightedChooser) GetAndRemove() (interface{}, error) {
 	if w.totalWeight == 0 {
 		return nil, errors.New("cannot choose from zero weight")
 	}
@@ -42,7 +42,7 @@ func (w *weightedChooser) GetAndRemove() (interface{}, error) {
 }
 
 // Based on https://eli.thegreenplace.net/2010/01/22/weighted-random-generation-in-python/
-func (w *weightedChooser) weightedChoice() (int, error) {
+func (w *WeightedChooser) weightedChoice() (int, error) {
 	rnd := w.rng.Intn(w.totalWeight)
 	for i, weight := range w.weights {
 		rnd -= weight
@@ -53,7 +53,7 @@ func (w *weightedChooser) weightedChoice() (int, error) {
 	return 0, errors.New("internal error - code should not reach this point")
 }
 
-func (w *weightedChooser) Remove(i int) {
+func (w *WeightedChooser) Remove(i int) {
 	w.totalWeight -= w.weights[i]
 
 	w.weights[i] = w.weights[len(w.weights)-1]
@@ -64,6 +64,6 @@ func (w *weightedChooser) Remove(i int) {
 	w.entries = w.entries[:len(w.entries)-1]
 }
 
-func (w *weightedChooser) Size() int {
+func (w *WeightedChooser) Size() int {
 	return len(w.entries)
 }

utils/weighted_random_chooser_test.go

@@ -6,7 +6,7 @@ import (
 )
 
 var _ = Describe("WeightedRandomChooser", func() {
-	var w *weightedChooser
+	var w *WeightedChooser
 	BeforeEach(func() {
 		w = NewWeightedRandomChooser()
 		for i := 0; i < 10; i++ {