Рекомендательные системы для игр

Рекомендательные системы увеличивают engagement и retention через персонализированный catalog navigation.

Подходы

Collaborative filtering: users с похожими предпочтениями. Content-based: similar features игр (thema, provider, volatility). Hybrid: комбинация. Deep learning: neural collaborative filtering, transformers (BERT4Rec). Contextual: учёт time-of-day, device, session context.

Данные

Implicit feedback: play time, sessions, launches. Explicit: favorites, ratings (реже). Contextual: device, time, geography. Content metadata: theme, mechanic, volatility, RTP. Cold start problem: новые players и новые games требуют fallback strategies.

Метрики

CTR (click-through rate) на recommendations. Session diversity: не только known favorites. Retention lift: A/B vs baseline. Coverage: % catalog в recommendations. Novelty: recommendations новых для user games. Business: revenue per session.

Реализация

Batch: nightly training + serving via Redis. Real-time: TensorFlow Serving, TorchServe. Feature engineering pipeline: Spark. Online learning для быстрой адаптации. Guardrails: не recommend excluded games, respect responsible gambling limits.