Publish current app and server
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ponzischeme89
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package recommend
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import (
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"math"
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"strings"
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"time"
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)
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// ContextAffinityProfile captures what a viewer tends to watch in broad local-time
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// windows. It is intentionally compact: seven weekdays by four day parts is enough to
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// learn household routines without pretending that a small history is a precise model.
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type ContextAffinityProfile struct {
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Slots map[string]ContextAffinityBucket `json:"slots,omitempty"`
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}
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type ContextAffinityBucket struct {
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Samples int `json:"samples"`
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GenreWeights map[string]float64 `json:"genres,omitempty"`
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StudioWeights map[string]float64 `json:"studios,omitempty"`
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}
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func NewContextAffinityProfile() ContextAffinityProfile {
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return ContextAffinityProfile{Slots: map[string]ContextAffinityBucket{}}
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}
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// Add records one matched Tracearr session. Completion and recency determine how much
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// taste evidence it contributes, while Samples controls confidence separately.
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func (p *ContextAffinityProfile) Add(
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item Item,
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started time.Time,
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completion float64,
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recencyPosition int,
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location *time.Location,
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) {
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if started.IsZero() {
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return
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}
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if p.Slots == nil {
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p.Slots = map[string]ContextAffinityBucket{}
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}
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if location == nil {
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location = time.Local
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}
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local := started.In(location)
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key := contextSlotKey(local.Weekday(), dayPart(local.Hour()))
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bucket := p.Slots[key]
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if bucket.GenreWeights == nil {
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bucket.GenreWeights = map[string]float64{}
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}
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if bucket.StudioWeights == nil {
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bucket.StudioWeights = map[string]float64{}
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}
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bucket.Samples++
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weight := (0.2 + clamp01(completion)) * math.Pow(0.985, float64(recencyPosition))
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for _, genre := range item.Genres {
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if genre = strings.TrimSpace(genre); genre != "" {
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bucket.GenreWeights[genre] += weight
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}
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}
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for _, studio := range item.Studios {
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if name := strings.TrimSpace(studio.Name); name != "" {
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bucket.StudioWeights[name] += weight * 0.4
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}
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}
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p.Slots[key] = bucket
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}
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// Score returns a bounded contextual affinity source score and its confidence. Exact
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// weekday/time behavior matters most; neighboring time windows and the same time on
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// other days provide progressively weaker fallbacks. No match simply returns zero.
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func (p ContextAffinityProfile) Score(
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item Item,
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now time.Time,
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location *time.Location,
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) (float64, float64) {
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if len(p.Slots) == 0 {
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return 0, 0
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}
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if location == nil {
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location = time.Local
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}
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local := now.In(location)
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part := dayPart(local.Hour())
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var score, sampleWeight float64
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for key, bucket := range p.Slots {
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weekday, bucketPart, ok := parseContextSlotKey(key)
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if !ok {
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continue
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}
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weight := contextSlotSimilarity(local.Weekday(), part, weekday, bucketPart)
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if weight == 0 {
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continue
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}
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var affinity float64
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for _, genre := range item.Genres {
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affinity += weightFold(bucket.GenreWeights, genre)
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}
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if n := len(item.Genres); n > 1 {
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affinity /= math.Sqrt(float64(n))
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}
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for _, studio := range item.Studios {
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affinity += weightFold(bucket.StudioWeights, studio.Name)
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}
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score += affinity * weight
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sampleWeight += float64(bucket.Samples) * weight
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}
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if sampleWeight == 0 {
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return 0, 0
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}
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// Five effective sessions are enough for the full (still bounded) contextual nudge.
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return score / sampleWeight, math.Min(1, sampleWeight/5)
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}
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// Contextualized returns a copy of the base profile with a modest current-time taste
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// nudge. It is used by dynamic shelves; the prepared For You pool applies the same
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// signal directly to candidate placement.
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func (p ContextAffinityProfile) Contextualized(
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base Profile,
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now time.Time,
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location *time.Location,
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) Profile {
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out := base
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out.GenreWeights = cloneWeights(base.GenreWeights)
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out.StudioWeights = cloneWeights(base.StudioWeights)
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probe := Item{Genres: keysFromWeights(out.GenreWeights)}
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_, confidence := p.Score(probe, now, location)
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if confidence == 0 {
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return out
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}
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local := now
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if location != nil {
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local = now.In(location)
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}
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part := dayPart(local.Hour())
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for key, bucket := range p.Slots {
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weekday, bucketPart, ok := parseContextSlotKey(key)
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if !ok {
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continue
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}
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similarity := contextSlotSimilarity(local.Weekday(), part, weekday, bucketPart)
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if similarity == 0 {
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continue
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}
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scale := 0.35 * confidence * similarity / math.Max(1, float64(bucket.Samples))
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for genre, weight := range bucket.GenreWeights {
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out.GenreWeights[genre] += weight * scale
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}
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for studio, weight := range bucket.StudioWeights {
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out.StudioWeights[studio] += weight * scale
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}
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}
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return out
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}
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func contextSlotSimilarity(currentDay time.Weekday, currentPart int, day time.Weekday, part int) float64 {
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dayDistance := int(currentDay) - int(day)
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if dayDistance < 0 {
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dayDistance = -dayDistance
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}
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if dayDistance > 3 {
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dayDistance = 7 - dayDistance
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}
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partDistance := currentPart - part
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if partDistance < 0 {
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partDistance = -partDistance
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}
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switch {
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case dayDistance == 0 && partDistance == 0:
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return 1
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case dayDistance == 0 && partDistance == 1:
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return 0.35
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case dayDistance == 1 && partDistance == 0:
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return 0.25
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case partDistance == 0:
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return 0.12
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default:
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return 0
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}
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}
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func dayPart(hour int) int {
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switch {
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case hour >= 5 && hour < 11:
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return 0
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case hour >= 11 && hour < 17:
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return 1
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case hour >= 17 && hour < 22:
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return 2
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default:
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return 3
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}
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}
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func contextSlotKey(day time.Weekday, part int) string {
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return string(rune('0'+day)) + ":" + string(rune('0'+part))
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}
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func parseContextSlotKey(key string) (time.Weekday, int, bool) {
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if len(key) != 3 || key[1] != ':' || key[0] < '0' || key[0] > '6' ||
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key[2] < '0' || key[2] > '3' {
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return 0, 0, false
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}
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return time.Weekday(key[0] - '0'), int(key[2] - '0'), true
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}
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func clamp01(value float64) float64 {
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if value < 0 {
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return 0
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}
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if value > 1 {
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return 1
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}
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return value
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}
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func cloneWeights(source map[string]float64) map[string]float64 {
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out := make(map[string]float64, len(source))
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for key, value := range source {
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out[key] = value
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}
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return out
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}
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func keysFromWeights(source map[string]float64) []string {
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out := make([]string, 0, len(source))
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for key := range source {
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out = append(out, key)
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}
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return out
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}
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