Develop a logistic regression model for each user to predict if a piece of content should be recommended, considering the user's past interactions with similar articles.

Manually categorize a subset of articles into predefined topics, then use these categories to train a supervised learning model, such as an SVM classifier, for grouping and recommending additional articles based on topic similarity.

Implement a collaborative filtering system that suggests content based on the user's past behavior and the behavior of similar users, leveraging user-article interaction data.

Use word2vec to transform articles into high-dimensional vectors based on their content, then create a model that recommends articles based on the cosine similarity between vectors, allowing for dynamic and content-based recommendations.