Solution to Scalability and Sparsity Problems in Collaborative Filtering using K-Means Clustering and Weight Point Rank (WP-Rank

Collaborative filtering is a method that can be used in recommendation systems. Collaborative Filtering works by analyzing rating data patterns. It is also used to make predictions of interest to users. This process begins with collecting data and analyzing large amounts of information on the behavior, activities, and tendencies of users. The results of the analysis are used to predict what users like based on similarities with other users. In addition, collaborative filtering is able to produce recommendations of better quality than recommendation systems based on content and demographics. However, collaborative filtering still faces scalability and sparsity problems. It are because the data is always evolving so that it becomes big data, besides that there are many data with incomplete conditions or many vacancies are found. Therefore, the purpose of this study proposed a clustering and ranking-based approach. The cluster algorithm used K-Means. Meanwhile, the WP-Rank method was used for ranking based. The experimental results showed that the running time was faster with an average execution time of 0.15 seconds by clustering. Furthermore, it was able to improve the quality of the recommendations, as indicated by an increase in the value of NDCG at k=22, the average value of NDCG was 0.82, so the recommendations produced were higher quality and more appropriate to the interests of the users.  Collaborative Filtering is a method to be used in recommendation systems. Collaborative Filtering works by analyzing rating data patterns. It is also used to make predictions of user interest. This process begins with collecting data and analyzing large amounts of information about the behavior, activities, and tendencies of users. The results of the analysis are used to predict what users like based on similarities with other users. In addition, Collaborative Filtering is able to produce recommendations with better quality than recommendation systems based on content and demographics. However, Collaborative Filtering still faces scalability and sparsity problems. It is because the data is always evolving so that it becomes big data, besides that there are many data with incomplete conditions or many vacancies are found. Therefore, the purpose of this study proposed a clustering and ranking based approach. The cluster algorithm used K-Means. Meanwhile, the WP-Rank method was used for ranking based. The experimental results showed that the running time was faster with an average execution time of 0.15 second by clustering. In addition, it was able to improve the quality of recommendations as indicated by an increase in the value of NDCG at k=22, the average value of NDCG was 0.82, so that the recommendations produced had more quality and more appropriate with user interests. &nbsp