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In this report, we describe an ensemble approach with a set of enhanced random forest models for COVID-19 retweet prediction challenge at CIKM Analyticup 2020 held by the 29th ACM International Conference on Information and Knowledge Management. The proposed approach is based on a global model and a set of personalized models. The global model consists of a set of random forests enhanced by three different types of models such as linear regression, feed-forward neural networks, and factorization machines. In addition to this global model, we trained a number of personalized models for users that exist in both training and test sets and have a sufficient number of tweets for training. Our approach obtained a MSLE (Mean Squared Log Error) value of 0.149997 on the test set of the challenge and ranked 4th on the final leaderboard.