Sistem Rekomendasi Linimasa Facebook Berdasarkan Topik Kesukaan Pengguna Menggunakan Metode Content-Based Filtering & Term Frequency-Inverse Document Frequency
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Abstract
This research aims to identify Facebook timeline recommendation systems based on user habits using Content-Based Filtering (CBF) and Term Frequency-Inverse Document Frequency (TF-IDF) methods. By utilizing historical user activity data, this system will customize content recommendations for individual users. The CBF method is used to compare the similarity of the content to be recommended with the user's habits, while TF-IDF is used to evaluate various keywords in the content. The results of this study show that the combination of CBF and TF-IDF methods can improve the accuracy of recommendation in Facebook timeline, resulting in a more convenient and relevant user experience.
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