Recommender Systems are systems that aid users in finding relevant items, products, or services, usually in an online setting. Collaborative Filtering is the most popular approach for building recommender system due to its superior performance. There are several collaborative filtering methods developed, however, all of them have an inherent problem of data sparsity. Covering Reduction Collaborative Filtering (CRCF) is a new collaborative filtering method developed to solve the problem. CRCF has a key feature called popular items extraction algorithm which produces a list of items with the most ratings, however, the algorithm fails in a denser dataset because it allows any item to be in the list. Likewise, the algorithm does not consider the rating values of items while considering the popular items. These make it to produce less accurate recommendation. This research extends CRCF by developing a new popular item extraction algorithm that removes items with low modal ratings and similarly utilizes the rating values in considering the popular items. This newly developed method is incorporated in CRCF and the new method is called Improved Popular Items Extraction for Covering Reduction Collaborative Filtering (ICRCF). Experiment was conducted on Movielens-1M and Movielens-10M datasets using precision, recall and f1-score as performance metrics. The result of the experiment shows that the new method, ICRCF provides a better recommendation than the base method CRCF in all the performance metrics. Furthermore, the new method is able to perform well both at higher and lower levels of sparsity.