One of the most prevalent recommendation systems is ranking-oriented collaborative filtering which employs ranking aggregation. The collaborative filtering study recently applied the ranking aggregation that considers the weight point of items to achieve a more accurate recommended ranking. However, this algorithm suffers in the execution time with an increased number of items. Therefore, this study proposes a new recommendation algorithm that combines the matrix decomposition method and ranking aggregation to reduce the time complexity. The matrix decomposition method utilizes singular decomposition value (SVD) to predict the unrated items. The ranking aggregation method applies weight point rank (WPR) to obtain the recommended items. The experimental results with the MovieLens 100K dataset result in a faster running time of 13.502 s. In addition, the normalized discounted cumulative gain (NDCG) score increased by 27.11% compared to the WP-Rank algorithm.