📅 18 July 2024
DOI: 10.62951/repeater.v2i3.140
Implementasi Item-Based Collaborative Filtering Untuk Rekomendasi Film
Repeater : Publikasi Teknik Informatika dan Jaringan
Asosiasi Riset Teknik Elektro dan Informatika Indonesia (ARTEII)
📄 Abstract
Item-based collaborative filtering is a popular technique in recommendation systems that aims to provide suggestions for films to watch or services to users based on similarities between items. In this approach, the similarity between items is calculated using metrics such as cosine similarity, allowing the prediction of user preferences for items that have never been rated. This research implements Item-based collaborative filtering using datasets from Kaggle. Experimental results show that the resulting model is able to provide recommendations with significant improvements in evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 3.05 and 3.26. This shows that the smaller the value, the better.
🔖 Keywords
#Item Based Collaborative Filtering; Recommendations; Film Analysis
ℹ️ Informasi Publikasi
Tanggal Publikasi
18 July 2024
Volume / Nomor / Tahun
Volume 2, Nomor 3, Tahun 2024
📝 HOW TO CITE
Rayhan Rizal Mahendra; Fetty Tri Anggraeny; Henni Endah Wahanani, "Implementasi Item-Based Collaborative Filtering Untuk Rekomendasi Film," Repeater : Publikasi Teknik Informatika dan Jaringan, vol. 2, no. 3, Jul. 2024.
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