Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a recommendation model that will suggest articles to readers that are similar to the articles they are currently reading. Which approach should you use?

A. Create a collaborative filtering system that recommends articles to a user based on the user’s past behavior. B. Encode all articles into vectors using word2vec, and build a model that returns articles based on vector similarity. C. Build a logistic regression model for each user that predicts whether an article should be recommended to a user. D. Manually label a few hundred articles, and then train an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories.