Develop a logistic regression model for each user to predict if an article should be recommended, considering the high computational cost and maintenance overhead.

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.

Manually categorize a few hundred articles, then train an SVM classifier to classify additional articles into these categories, despite the scalability and update challenges.

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.