To achieve the goal of providing a tailored experience for users, the site can employ several strategies:

Collecting browsing data: By requiring users to log in, the site can track their browsing history and collect data on the topics they are interested in. This data can then be used to understand user preferences and make personalized recommendations.
Building a recommendation system: The site can use machine learning algorithms to analyze the collected data and generate recommendations based on user preferences. This can involve techniques such as collaborative filtering, content-based filtering, or hybrid approaches.
Utilizing embeddings: The site can use embeddings, which are numerical representations of topics or user preferences, to measure the similarity or distance between different topics. By considering the embeddings of topics that users have shown interest in, the site can recommend related topics that users may find interesting.
Incorporating causal relationships: In addition to considering the distance between embeddings, the site can also analyze the causal relationships in users' browsing history. For example, if a user frequently clicks on topics related to a specific subject, the site can infer that the user is interested in that subject and provide more relevant recommendations.
Continuous improvement: Providing a tailored experience requires ongoing work and refinement. The site can regularly update and improve its recommendation algorithms based on user feedback and new data. This iterative process can help ensure that users receive more accurate and relevant recommendations over time.

By implementing these strategies, the site aims to provide users with a more personalized and engaging browsing experience, allowing them to discover and explore topics that align with their interests.

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