As a product manager, the goals I would set for Reels recommendation engine would be to increase user engagement and retention on the platform. To achieve this, I would focus on improving the accuracy and relevance of the video recommendations that the users receive. One key metric to track would be the click-through rate (CTR) of the recommended videos. A higher CTR would indicate that users are finding the recommended videos more interesting and relevant, which could lead to longer viewing sessions and increased engagement with the platform. Another metric to track would be the retention rate of users who engage with the recommended videos. If users are more likely to continue using the platform after watching recommended videos, then it would be a sign that the recommendation engine is doing its job well. To improve the accuracy and relevance of the recommendations, I would explore different algorithms and data sources to train the recommendation engine. This could include leveraging user data such as viewing history, likes, comments, and shares, as well as external data sources such as trending topics and popular videos. Additionally, I would work with the data science team to continuously test and optimize the recommendation algorithms to ensure that they are delivering the best possible results for the users. Overall, the goal of the Reels recommendation engine would be to create a personalized and engaging video experience for each user, which would ultimately drive user retention and growth for the platform.