{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T20:44:50Z","timestamp":1775421890000,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T00:00:00Z","timestamp":1633478400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Indonesia Endowment Fund for Education (LPDP), Ministry of Finance of Republic of Indonesia: Beasiswa Unggulan Dosen Indonesia - Dalam Negeri (BUDI-DN)","award":["20200421211035"],"award-info":[{"award-number":["20200421211035"]}]},{"name":"Directorate General of Higher Education (Dikti), Ministry of Education, Culture, Research and Technology, Research Grant: Penelitian Disertasi Doktor","award":["2313\/UN1\/DITLIT\/DIT-LIT\/PT\/2021"],"award-info":[{"award-number":["2313\/UN1\/DITLIT\/DIT-LIT\/PT\/2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/computers10100123","type":"journal-article","created":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T09:24:04Z","timestamp":1633598644000},"page":"123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Recommendation Algorithm Using Clustering-Based UPCSim (CB-UPCSim)"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6104-6692","authenticated-orcid":false,"given":"Triyanna","family":"Widiyaningtyas","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia"},{"name":"Department of Electrical Engineering, Universitas Negeri Malang, Malang 65145, Indonesia"}]},{"given":"Indriana","family":"Hidayah","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia"}]},{"given":"Teguh Bharata","family":"Adji","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,6]]},"reference":[{"key":"ref_1","unstructured":"Kherad, M., and Bidgoly, A.J. 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