Представления пользователя и поста для мультимодальной ленты
Представления пользователя и поста для мультимодальной ленты
Ответить самому
Сначала сформулируйте ответ как на собеседовании, затем откройте разбор и оцените себя.
Короткий ответ
Start with interaction history, user profile features and post content features: category, text embeddings, image embeddings and simple statistics. Combine collaborative and content baselines before training a heavier ranker.
Полный разбор
Represent posts with both structured and unstructured features: topic/category, author, age, language, text length, text embedding, image embedding, moderation/safety flags and early engagement statistics that are available at serving time.
Represent users with recent interaction history, long-term topic preferences, followed authors, profile or segment features that are allowed for this product, and aggregated embeddings of posts they engaged with. For a bank app, treat sensitive features carefully and avoid using them without a clear policy and fairness review.
A practical baseline is a hybrid candidate generator such as LightFM or ALS with content features, plus popularity/freshness fallback. Then add a ranker that combines candidate score, user-post affinity, freshness, author features and engagement signals. The baseline should be simple enough to debug and strong enough to collect better logs.
Теория
A cold social-feed recommender needs hybrid collaborative and content signals because both new users and new posts are common.
Типичные ошибки
- Use only post embeddings and ignore user history.
- Use bank profile features without discussing policy constraints.
- Train on engagement stats that are not available at serving time.
- Skip a simple baseline and jump directly to a deep two-tower model.
Как отвечать на собеседовании
- Separate user features, item features and interaction features.
- Mention cold start for both users and posts.