Collaborative filtering recommender systems being the most successful and widely used plays an important role in providing suggestions or recommendations to users for the items of interest. However, many of these systems recommend items to individual users based on ratings which may not be possible if they are not sufficient due to the following problems: it may lead to the prediction of uninterested popular items already known to the users because of the penalty function employed to punish those items, the sparsity of the user-item rating matrix increases making it difficult to provide accurate recommendations and also it ignores the users general preferences on the recommended items whether they are of interest to users or not. Therefore, many times uninterested items can be found in the recommended lists of an individual user. This will make user to lose interest in the recommendations if these uninterested predicted items always appear in the lists. In this paper, we proposed a collaborative filtering recommendations refinement framework that combines the solutions to these three identified problems. The framework incorporates a popularise similarity function to reduce the influence of popular items during recommendations, an algorithm to fill up the missing ratings of unwanted recommendations in the user-item rating matrix thereby reducing the sparsity problem and finally an algorithm to solicit for user feedback on the recommended items to minimise uninterested recommendations.