Perfecting the recommendation system. The platform will suggest strategies based on users’ goals and risk tolerance. Working to ensure every recommendation is as helpful and well-founded as possible.

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Absolutely! Combining quantitative data with qualitative insights is essential for building a truly effective recommendation system. By factoring in both market trends and individual user preferences, the platform can offer tailored strategies that feel more relevant and trustworthy. Have you considered adding any user feedback loops to further refine the recommendations?

That’s a solid approach! Tailoring recommendations based on users' goals and risk tolerance will make the platform much more personalized and effective. To ensure recommendations are useful and justified, it’s key to combine both quantitative data (such as past performance, volatility, and market trends) and qualitative insights (like user preferences and behavioral patterns). This will not only guide users to the right strategies but also build trust in the system over time.