Tell us about the personalized tariff recommendation module — what was the logic behind it, and who made the decision to recommend?

The personalized tariff recommendation module is usually built based on user data analysis and business rules. The logic may include:

• Collecting user data: current tariff, usage history, preferences, demographics.
• Analyzing behavior and needs: for example, how many minutes, traffic, or SMS the user uses.
• Matching with available tariffs and identifying the most advantageous or suitable ones.
• Applying prioritization rules: for example, recommending a tariff with the best price/quality ratio or with promotions.

The decision on the recommendation can be made by:

• Automated algorithm — based on machine learning or rules.
• Business logic — strictly defined conditions.
• Hybrid approach — the algorithm offers options, and business rules filter or adjust them.

For example, if a user frequently exceeds the traffic limit, the system may recommend a tariff with a larger internet package.

In a Java module, this could be a service that receives the user profile, calculates tariff scoring, and returns a list of recommendations. The decision is made within the service, possibly using ML models or rules implemented in the code.