Comparison of Borda and NRF (Normalized Rating Frequency) in Recommender System
DOI:
https://doi.org/10.55123/jomlai.v1i3.1026Kata Kunci:
Borda, NRF, Recommender System, ComparisonAbstrak
The Collaborative Filtering method is a popular method in making recommender systems. Although CF is a popular method, it has major problems, namely cold start and sparsity . Several studies have been conducted to treat cold starts and sparsity. One way to overcome cold start and sparsity is the Borda calculation method. Research using the Borda method has been carried out a lot but has not utilized the rating optimally. The NRF method is a new method offered to maximize the use of ratings. By using dummy test data, the NRF method is more effective than Borda in calculating recommendation scores.
Referensi
S. Lestari, T. B. Adji, and A. E. Permanasari, “Performance Comparison of Rank Aggregation Using Borda and Copeland in Recommender System,” 2018 Int. Work. Big Data Inf. Secur., pp. 69–74, 2018.
H. T. Hidayat and N. G. A. P. H. Saptarini, “Sistem Pendukung Keputusan Pemilihan Maskapai Terbaik Pada Rute Domestik Dengan Menggunakan Metode Multi Factor Evaluation Process (MFEP),” MATRIX, vol. 4, no. 3, 2014.
P. I. Ciptayani and N. G. A. P. H. Saptarini, “Sistem Pendukung Keputusan Pemilihan Manajer Proyek Menggunakan Analytical Hierarchy Process,” MATRIX, vol. 4, no. 2, 2014.
S. K. Gorakala and M. Usuelli, Building a Recommnebder System with R. Birmingham-Mumbai: Packt Publishing Ltd., 2015.
S. Natarajan, S. Vairavasundaram, S. Natarajan, and A. H. Gandomi, “Resolving Data Sparsity and Cold Start Problem in Collaborative Filtering Recommender System Using Linked Open Data,” Expert Syst. Appl., 2020.
A. S. Girsang, B. AL Faruq, H. R. Herlianto, and S. Simbolon, “Collaborative Recommendation System in Users of Anime Films,” in Journal of Physics: Conference Series, 2020, pp. 1–7.
R. Hidayat, “Membangun Sistem Rekomendasi Berbasis Algoritma Apriori Pada Website Penjualan UD Rahmat,” STMIK Widya Cipta Dharma, 2019.
W. A. Prakoso, “Pembangunan Sistem Rekomendasi Dalam Strategi Bisnis Umkm Berdasarkan Lokasi Di Kota Bandung Pada Platform Android,” Universitas Komputer Indonesia, 2019.
A. Wiyanto, “Sistem Rekomendasi Kuliner Dengan Menggunakan Metode K-Means Untuk Mendukung Strategi Penjualan Makanan,” Universitas Pembangunan Nasional Veteran Jakarta, 2019.
N. P. Sari, C. A. Putra, and F. Muttaqin, “Rancang Bangun Sistem Rekomendasi Pencarian Produk Properti Dengan Menggunakan Metode Association Rule Di PT.Brighton,” J. Inform. dan Sist. Inf., vol. 01, no. 1, 2020.
K. R. Sari, W. Suharso, and Y. Azhar, “Pembuatan Sistem Rekomendasi Film dengan Menggunakan Metode Item Based Collaborative Filtering pada Apache Mahout,” REPOSITOR, vol. 2, no. 6, 2020.
P. Nastiti, “Penerapan Metode Content Based Filtering Dalam Implementasi Sistem Rekomendasi Tanaman Pangan,” TEKNIKA, vol. 8, no. 1, 2019.
A. S. Guntara, “Implementasi Metode Naïve Bayes pada Sistem Rekomendasi Pembelian Mobil pada PT. Honda Prospect Motor,” REMIK, vol. 4, no. 1, 2019.
I. T. Sevandri, R. K. Dewi, and M. T. Ananta, “Implementasi Algoritma Topsis Pada Sistem Rekomendasi Pencarian Lokasi Gym Berbasis Android (Studi Kasus: Kota Malang),” JPTIIK, vol. 3, no. 4, 2019.
T. W. Harjanti and L. Lipindi, “Rancang Bangun Sistem Rekomendasi BIrotravel pada Fitur Custom Trip untuk Aplikasi Travel Vaithme Menggunakan Algoritma Weighted Product Berbasis Android,” in SEMNASTEK 2019, 2019.
S. Wiyono and T. Abidin, “COMPARATIVE STUDY OF MACHINE LEARNING KNN , SVM , AND DECISION TREE ALGORITHM TO PREDICT STUDENT ’ S PERFORMANCE,” IJRG, vol. 7, no. January, pp. 190–196, 2019.
maryam K. Najafabadi, A. Mohamed, and C. W. Onn, “An impact of time and item influencer in collaborative filtering recommendations using graph-based model,” Inf. Process. Manag., vol. 56, no. 3, pp. 526–540, 2019.
B. Lika, K. Kolomvatsos, and S. Hadjiefthymiades, “Expert Systems with Applications Facing the cold start problem in recommender systems,” Expert Syst. Appl., vol. 41, no. 4, pp. 2065–2073, 2014.
L. Uyangoda, S. Ahangama, and T. Ranasinghe, “User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation,” in Thirteenth International Conference on Digital Information Management (ICDIM), 2018, pp. 24–28.
Y. Tang and Q. Tong, “BordaRank : A Ranking Aggregation Based Approach to Collaborative Filtering,” in International Conference on Computer and Information Science (ICIS), 2016, pp. 1–6.
G. Al-sharrah, “Ranking Using the Copeland Score : A Comparison with the Hasse Diagram,” J. Chem. Inf. Model., pp. 785–791, 2010.
S. Lestari, T. B. Adji, and A. E. Permanasari, “NRF : Normalized Rating Frequency for Collaborative Filtering Paper,” in International Conference on Applied Information Technology and Innovation (ICAITI), 2018, pp. 19–25.
Unduhan
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2022 Taufiq Abidin, Slamet Wiyono
Artikel ini berlisensiCreative Commons Attribution-ShareAlike 4.0 International License.
Copyright (c) 2022 The authors. Published by Yayasan Literasi Indonesia 
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License
The author(s) whose article is published in the JOMLAI journal attain the copyright for their article and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By submitting the manuscript to JOMLAI, the author(s) agree with this policy. No special document approval is required.
The author(s) guarantee that:their article is original, written by the mentioned author(s),
- has never been published before,
- does not contain statements that violate the law, and
- does not violate the rights of others, is subject to copyright held exclusively by the author(s), and is free from the rights of third parties, and that the necessary written permission to quote from other sources has been obtained by the author(s).
The author(s) retain all rights to the published work, such as (but not limited to) the following rights:
- Copyright and other proprietary rights related to the article, such as patents,
- The right to use the substance of the article in its own future works, including lectures and books,
- The right to reproduce the article for its own purposes,
- The right to archive all versions of the article in any repository, and
- The right to enter into separate additional contractual arrangements for the non-exclusive distribution of published versions of the article (for example, posting them to institutional repositories or publishing them in a book), acknowledging its initial publication in this journal (JOMLAI: Journal of Machine Learning and Artificial Intelligence).
Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. JOMLAI will not be held responsible for anything that may arise because of the writer's internal dispute. JOMLAI will only communicate with correspondence authors.
Authors should also understand that their articles (and any additional files, including data sets, and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. JOMLAI allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JOMLAI to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published
