Google Professional Machine Learning Engineer
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Ultimate access to all questions.
Your company operates a global news aggregation application that collects articles from various online sources and distributes them to users in real-time. The application has millions of users with diverse reading preferences. You are tasked with developing a recommendation model to suggest articles similar to those a user is currently reading, ensuring the solution is scalable, cost-effective, and capable of handling the dynamic nature of news content. Which strategy would you employ? (Choose one correct option)
A
Develop a logistic regression model for each user to predict if an article should be recommended, considering the high computational cost and maintenance overhead.
B
Implement a collaborative filtering system that suggests articles based on the user's historical behavior, ignoring the cold start problem for new users and articles.
C
Manually categorize a few hundred articles, then train an SVM classifier to classify additional articles into these categories, despite the scalability and update challenges.
D
Convert all articles into vectors using word2vec, and construct a model that recommends articles based on vector similarity, leveraging the efficiency and scalability of this approach.