Triyanna Widiyaningtyas, Indriana Hidayah, Teguh Bharata Adji
One of the well-known recommendation systems is memory-based collaborative filtering that utilizes similarity metrics. Recently, the similarity metrics have taken into account the user rating and user behavior scores. The user behavior score indicates the user preference in each product type (genre). The added user behavior score to the similarity metric results in more complex com-putation. To reduce the complex computation, we combined the clustering method and user behavior score-based similarity. The clustering method applies k-means clustering by determination of the number of clusters using the Silhouette Coefficient. Whereas the user behavior score-based similarity utilizes User Profile Correlation-based Similarity (UPCSim). The experimental results with the MovieLens 100k dataset showed a faster computation time of 4.16 s. In addition, the Mean Ab-solute Error (MAE) and Root Mean Square Error (RMSE) values decreased by 1.88% and 1.46% com-pared to the baseline algorithm. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia; Department of Electrical Engineering, Universitas Negeri Malang, Malang, 65145, Indonesia
