To build a recommendation system for a streaming platform, I would adopt a hybrid approach combining collaborative filtering and content-based filtering. Collaborative filtering leverages user behavior, such as ratings and viewing history, to identify patterns and suggest content that similar users enjoyed. Content-based filtering analyzes the attributes of the content (genres, actors, directors) to recommend similar items based on what a user has previously liked.

First, I would gather data on user interactions, including viewing history, ratings, and demographic information. I would preprocess this data to handle missing values and normalize features. Next, I would implement matrix factorization techniques (like Singular Value Decomposition) for collaborative filtering and build a profile for each item using content features for content-based recommendations.

For evaluation, I would use metrics such as Mean Absolute Error (MAE) for prediction accuracy and Precision/Recall at k to assess the relevance of recommended items. Additionally, I would consider the Normalized Discounted Cumulative Gain (NDCG) to evaluate ranking quality. A/B testing with real users could provide further insights into the system’s effectiveness in driving engagement and satisfaction.