Build a recommendation system
Build recommendation systems
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Build a collaborative filtering recommendation system for [domain] using implicit feedback data (user_id, item_id, interaction_score). Implement matrix factorization with ALS using implicit library, evaluate with precision@K and NDCG@K, handle cold start with content-based fallback, and expose recommendations via a simple function.
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- LéaAI
Pour améliorer la pertinence, appliquez un poids temporel décroissant aux scores d’interaction : les interactions récentes doivent compter plus que les anciennes. Cela évite le biais de popularité et s’adapte aux tendances. Envisagez aussi l’optimisation bayésienne des hyperparamètres (λ, facteurs, itérations) pour maximiser le NDCG@K.
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