Relevant bibliographies by topics / CBF- Content-based filtering

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Academic literature on the topic 'CBF- Content-based filtering'

Author: Grafiati
Published: 2 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "CBF- Content-based filtering"

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PUTRA, KURNIA RAMADHAN, and MOHAMMAD ADITIYA RACHMAN. "Perbandingan Metode Content-based, Collaborative dan Hybrid Filtering pada Sistem Rekomendasi Lagu." MIND Journal 9, no. 2 (2024): 179–93. https://doi.org/10.26760/mindjournal.v9i2.179-193.

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AbstrakSistem rekomendasi dapat dimanfaatkan untuk membantu pengguna menemukan item atau informasi sesuai preferensi mereka, termasuk lagu. Metode seperti Collaborative Filtering (CF), Content-Based Filtering (CBF), dan Hybrid Filtering digunakan untuk meningkatkan kualitas rekomendasi berdasarkan interaksi pengguna dan karakteristik konten. Penelitian ini membandingkan efektivitas ketiga metode tersebut dalam rekomendasi lagu menggunakan dataset dengan 68.330 entri data. Metode CF dan CBF diterapkan secara terpisah, lalu dikombinasikan dalam pendekatan hybrid untuk mengevaluasi peningkatan ha
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Faurina, Ruvita, and Evlin Sitanggang. "Implementasi Metode Content-Based Filtering dan Collaborative Filtering pada Sistem Rekomendasi Wisata di Bali." Techno.Com 22, no. 4 (2023): 870–81. http://dx.doi.org/10.33633/tc.v22i4.8556.

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Sektor pariwisata memiliki peran penting dalam perekonomian Bali. Pada bulan April 2023, kunjungan wisatawan ke Bali mencapai 411.510, meningkat 11,01% dari bulan Maret 2023 (sumber: Badan Pusat Statistik Bali). Untuk memperkenalkan destinasi wisata yang ada, Bali perlu menggunakan teknologi yang sedang berkembang seperti sistem rekomendasi. Dalam hal ini, digunakan metode Content-based filtering (CBF) dan Collaborative Filtering (CF). CBF memberikan rekomendasi berdasarkan preferensi pengguna terhadap kategori destinasi wisata, sementara CF menggunakan data histori rating dari pengguna lain u
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Liu, Duen-Ren, Chuen-He Liou, Chi-Chieh Peng, and Huai-Chun Chi. "Hybrid content filtering and reputation-based popularity for recommending blog articles." Online Information Review 38, no. 6 (2014): 788–805. http://dx.doi.org/10.1108/oir-12-2013-0273.

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Purpose – Social bookmarking is a system which allows users to share, organise, search and manage bookmarks of web resources. However, with the rapid growth in the production of online documents, people are facing the problem of information overload. Social bookmarking web sites offer a solution to this by providing push counts, which are counts of users’ recommendations of articles, and thus indicate the popularity and interest thereof. In this way, users can use the push counts to find popular and interesting articles. A measure of popularity-based solely on push counts, however, cannot be c
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Juhi, Dhameliya, and Desai Nikita. "Job Recommendation System using Content and Collaborative Filtering Based Techniques." International Journal of Soft Computing and Engineering (IJSCE) 9, no. 3 (2019): 8–13. https://doi.org/10.35940/ijsce.C3266.099319.

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Internet based recruiting platforms decrease advertisement cost, but they suffer from information overload problem. The job recommendation systems (JRS) have achieved success in e-recruitment process but still they are not able to capture the complexity of matching between candidates’ desires and organizations’ requirements. Thus, we propose a hybrid JRS which combines recommendations of content-based filtering (CBF) and collaborative filtering (CF) to overcome their individual major shortcomings namely overspecialization and over-fitting. In proposed system, CBF model makes recomm
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Habibi, Roni, and Darfial Guslan. "RISIKO MAGANG MAHASISWA DENGAN PENDEKATAN ALGORITMA CONTENT-BASED FILTERING DAN SUPPORT VECTOR MACHINE." Competitive 20, no. 1 (2025): 12–23. https://doi.org/10.36618/competitive.v20i1.4195.

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Penelitian ini menggunakan pendekatan kuantitatif untuk mengkaji penggunaan algoritma Content-Based Filtering (CBF) dan Support Vector Machine (SVM) dalam manajemen risiko magang mahasiswa. Metode penelitian yang digunakan adalah Crisp-DM (Cross-Industry Standard Process for Data Mining) yang membantu peneliti mengorganisasikan dan menganalisis data dengan langkah-langkah yang terstruktur. Dalam penelitian ini, peneliti mengumpulkan data dengan menggunakan kuesioner yang dirancang khusus yang bertujuan untuk memperoleh informasi mengenai risiko-risiko yang mungkin terjadi selama mahasiswa maga
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Aranzamendez, Samantha Gwyn, Joshua Caleb Bolito, Aron Christoper Rafe, Jamillah Guialil, Dan Michael Cortez, and Raymund Dioses. "An Enhanced Content-based Filtering Using Maximal Marginal Relevance." International Journal of Computing Sciences Research 8 (January 1, 2024): 3070–87. https://doi.org/10.25147/ijcsr.2017.001.1.204.

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Purpose–The studyaims toenhance Content-based Filtering by diversifying its recommended items to combatoverspecialization. It traditionallyrecommends items that are directly related to the user profile, preventingusers from discovering newer sets ofitems. Method–Maximal Marginal Relevance isintegrated into the algorithm –are-ranking algorithm,developed by Carbonell and Goldsteinthat enhancesthe diversity of items retrieved by information retrieval systems–to enhance Content-based Filtering and address the underlying overspecialization problem. Results–By integrating Maximal Marginal Relevance,
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Vasyl, Lytvyn, Vysotska Victoria, Shatskykh Viktor, et al. "DESIGN OF A RECOMMENDATION SYSTEM BASED ON COLLABORATIVE FILTERING AND MACHINE LEARNING CONSIDERING PERSONAL NEEDS OF THE USER." Eastern-European Journal of Enterprise Technologies 4, no. 2 (100) (2019): 6–28. https://doi.org/10.15587/1729-4061.2019.175507.

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The paper reports a study into recommendation algorithms and determination of their advantages and disadvantages. The method for developing recommendations based on collaborative filtering such as Content-Based Filtering (CBF), Collaborative Filtering (CF), and hybrid methods of Machine Learning (ML) has been improved. The paper describes the design principles and functional requirements to a recommendation system in the form of a Web application for choosing the content required by user using movies as an example. The research has focused on solving issues related to cold start and scalabilit
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Hermanto, Nandang, Irma Darmayanti, Dimas Saputra, and Aden Hidayatuloh. "Development of Mobile Application by Applying Content-Based Filtering." sinkron 9, no. 1 (2025): 232–38. https://doi.org/10.33395/sinkron.v9i1.14320.

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The rapid advancements in information technology have transformed modern lifestyles, driving changes in consumer behavior and expectations, especially in the retail industry. This study focuses on developing a mobile application for Ampu Mart, a newly established retail business in Indonesia, to optimize product recommendation systems using the Content-Based Filtering (CBF) approach. The research integrates CBF with string matching and cosine similarity algorithms to provide personalized product recommendations based on customer preferences, enhancing user satisfaction and supporting more effi
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Aditya Nugraha, Anak Agung, and Ngurah Agus Sanjaya ER. "Penyusunan Sistem Rekomendasi Produk Diecast Mobil Dengan Metode Content-Based Filtering (CBF)." Jurnal Nasional Teknologi Informasi dan Aplikasnya 1, no. 3 (2023): 973. https://doi.org/10.24843/jnatia.2023.v01.i03.p25.

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The growing popularity of diecast car collections has created a demand for efficient recommendation systems to assist collectors in discovering new products. This study focuses on the development of a content-based filtering (CBF) recommendation system for diecast car products. The system employs the TF-IDF (Term Frequency-Inverse Document Frequency) and cosine similarity techniques to calculate the relevance between products and user preferences. By analyzing the textual features of diecast car products, such as brand, model, and specifications, the CBF system generates personalized recommend
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Manikandan, J. "Movie Recommendation System Mistreatment Current Trends and Sentiment Analysis from Micro Blogging Knowledge." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 393–98. http://dx.doi.org/10.22214/ijraset.2021.38651.

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Abstract: Recommendation systems (RSs) have garnered immense interest for applications in e-commerce and digital media. Traditional approaches in RSs include such as collaborative filtering (CF) and content-based filtering (CBF) through these approaches that have certain limitations, such as the necessity of prior user history and habits for performing the task of recommendation. To minimize the effect of such limitation, this article proposes a hybrid RS for the movies that leverage the best of concepts used from CF and CBF along with sentiment analysis of tweets from microblogging sites. The
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Conference papers on the topic "CBF- Content-based filtering"

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Azhan, Fadil Faithul, and Erwin Budi Setiawan. "Tourism Destination Recommendation System on Social Media X (Twitter) with Content-Based Filtering (CBF) and Gated Recurrent Unit (GRU) Approach." In 2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT). IEEE, 2024. https://doi.org/10.1109/comnetsat63286.2024.10862155.

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