A company is developing a system to recommend personalized news articles. The system's goal is to continuously adapt to a user's evolving interests. Two strategies are proposed:

Strategy A: Collect a massive, one-time dataset of user profiles and the articles they clicked on in the past. Train a model on this static dataset to predict which articles a new user is likely to click.

Strategy B: Design a system that learns through direct interaction. For each article it recommends, it receives a positive signal if the user clicks it and a negative signal if they ignore it. The system's objective is to maximize the positive signals it receives over time.

Evaluate these two strategies. Which one is better suited for creating a system that adapts to a user's evolving interests? Justify your evaluation by comparing the core mechanics of each strategy and their implications for long-term personalization.