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

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

collaborative filtering; scalability; sparsity, K-means; WP-rank

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.

Mohamad Fahmi Hafidz, Sri Lestari

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

August 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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