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To effectively assess the comparative efficiency of two backend engines used for the automated creation of Meta/Facebook "Friend" recommendations, a comprehensive approach is necessary. This involves a combination of A/B testing, defining clear metrics, and evaluating both system and functional performance. Below is a detailed strategy to achieve this:
By following this structured approach, you can comprehensively evaluate the efficiency of the two backend engines and make informed decisions to enhance the friend recommendation feature on Meta/Facebook.