Библиотека собеса по Data Science | вопросы с собеседований | United States America (US)

Create: 2025-05-08 Update: 2025-07-08 19:19:12

❓Как обнаружить и смягчить эффект популярности (popularity bias) в рекомендательной системе

Алгоритмы рекомендаций часто усиливают популярность уже популярных видео — их всё чаще показывают, в то время как новые или нишевые остаются незамеченными. Это создает эффект «богатые становятся богаче».

🔍 Как обнаружить

Посмотрите на логи рекомендаций — если небольшая доля контента получает основную массу показов, это тревожный сигнал. Обычно это «голова» распределения (head), тогда как «хвост» (long tail) игнорируется.

🛠 Методы смягчения

• Нормализация метрик (например, watch-time) с учетом числа показов — чтобы не усиливать положительную обратную связь.

• Поддержка длинного хвоста: в механизме отбора кандидатов добавить специальную логику, продвигающую менее популярные видео.

• Умное переупорядочивание (re-ranking): резервировать часть позиций в выдаче для менее популярных видео.

⚠️ Важно

• Слишком сильное наказание популярных видео может снизить удовлетворенность пользователя.

• Нельзя наказывать все тематики одинаково: специализированный контент может иметь честно низкие метрики, не из-за предвзятости, а из-за ниши.

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Библиотека собеса по Data Science | вопросы с собеседований

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Библиотека собеса по Data Science | вопросы с собеседований

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❓Как обнаружить и смягчить эффект популярности (popularity bias) в рекомендательной системе

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