Widiyaningtyas, Triyanna and Hidayah, Indriana and Adji, Teguh Bharata (2021) Recommendation Algorithm Using Clustering-Based UPCSim (CB-UPCSim). COMPUTERS, 10 (10). ISSN 2073-431X
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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 computation. 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 Absolute Error (MAE) and Root Mean Square Error (RMSE) values decreased by 1.88% and 1.46% compared to the baseline algorithm.</p>
Item Type: | Article |
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Uncontrolled Keywords: | collaborative filtering; memory-based; similarity metrics; k-means clustering; Silhouette Coefficient |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Faculty of Engineering > Electrical and Information Technology Department |
Depositing User: | Sri JUNANDI |
Date Deposited: | 19 Oct 2024 07:11 |
Last Modified: | 19 Oct 2024 07:11 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/9193 |