EVALUATION OF HYBRID MOVIE RECOMMENDATION SYSTEM BASED ON NEURAL NETWORKS
Abstract
Abstract: Recommendation systems are becoming increasingly important with the growth of streaming platforms. The purpose of this study is to compare the performance of Content-Based Filtering, Neural Collaborative Filtering, and a combination of both in a movie recommendation system. The method used in this study involves retrieving movie details from the TMDB API and ratings from the MovieLens 32M Dataset (2010-2023). Each model's performance is evaluated using evaluation metrics such as RMSE and MAE. The results of this study indicate that Neural Collaborative Filtering achieves the best prediction performance (RMSE = 0.785423, MAE = 0.581262), followed by the hybrid model (RMSE = 0.800863, MAE = 0.660872), while Content-Based Filtering produces low performance and limits the capabilities of the hybrid model. In conclusion, these findings highlight the superiority of latent feature-based models such as NCF that learn directly from user interaction patterns over content-based approaches in the context of modern recommendation systems.
Keywords: content-based filtering; hybrid filtering; movie recommendation; neural collaborative filtering.
Abstrak: Sistem rekomendasi menjadi semakin penting seiring berkembangnya platform streaming. Tujuan dari penelitian ini adalah membandingkan kinerja Content-Based Filtering, Neural Collaborative Filtering dan kombinasi keduanya dalam sistem rekomendasi film. Metode yang digunakan dalam penelitian ini melibatkan pengambilan detail film dari TMDB API dan rating dari dataset MovieLens 32M Dataset (2010-2023). Setiap peforma model dievaluasi dengan menggunakan metrik evaluasi seperti RMSE dan MAE. Hasil dari penelitian ini menunjukkan bahwa Neural Collaborative Filtering mencapai kinerja prediksi terbaik (RMSE = 0.785423, MAE = 0.581262), diikuti oleh model hybrid (RMSE = 0.800863, MAE = 0.660872), sementara Content-Based Filtering menghasilkankan peforma yang rendah dan membatasi kemampuan model hybrid. Kesimpulannya, penelitian ini menyoroti superiotas model berbasis latent feature seperti NCF yang belajar langsung dari pola interaksi pengguna dibandingkan pendekatan berbasis konten dalam konteks sistem rekomendasi modern.
Kata kunci: content-based filtering; hybrid filtering; neural collaborative filtering; rekomendasi film.
References
Anang Furkon RIfai and Erwin Budi Setiawan, “Memory-based Collaborative Filtering on Twitter Using Support Vector Machine Classification,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 5, pp. 702–709, Oct. 2022, doi: 10.29207/resti.v6i5.4270.
A. Nilla and E. B. Setiawan, “Film Recommendation System Using Content-Based Filtering and the Convolutional Neural Network (CNN) Classification Methods,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 10, no. 1, p. 17, Feb. 2024, doi: 10.26555/jiteki.v9i4.28113.
P. A. Sedyo Mukti and Z. K. A. Baizal, “Enhancing Neural Collaborative Filtering with Metadata for Book Recommender System,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 19, no. 1, p. 61, Jan. 2025, doi: 10.22146/ijccs.103611.
L. Wu, X. He, X. Wang, K. Zhang, and M. Wang, “A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation,” Apr. 2021, doi: 10.1109/TKDE.2022.3145690.
M. A. Pradana and A. T. Wibowo, “MOVIE RECOMMENDATION SYSTEM USING HYBRID FILTERING WITH WORD2VEC AND RESTRICTED BOLTZMANN MACHINES,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 9, no. 1, pp. 231–241, Feb. 2024, doi: 10.29100/jipi.v9i1.4306.
D. Velamentosa, E. Zuliarso, and J. Raya Tri Lomba Juang, “SISTEM REKOMENDASI FILM MENGGUNAKAN METODE CONTENT-BASED FILTERING,” 2025.
J. Aisyiah and L. Cahyani, “Sistem Rekomendasi Program Studi Menggunakan Metode Hybrid Recommendation (Studi Kasus: MAN Sumenep),” Jurnal Eksplora Informatika, vol. 12, no. 1, pp. 59–72, Jan. 2024, doi: 10.30864/eksplora.v12i1.992.
B. Drammeh and H. Li, “Enhancing neural collaborative filtering using hybrid feature selection for recommendation,” PeerJ Comput Sci, vol. 9, 2023, doi: 10.7717/peerj-cs.1456.
C. Li, I. Ishak, H. Ibrahim, M. Zolkepli, F. Sidi, and C. Li, “Deep Learning-Based Recommendation System: Systematic Review and Classification,” 2023, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ACCESS.2023.3323353.
D. Laras and H. Hasrullah, “Analisis Kinerja Sistem Rekomendasi Film Berbasis Deep Learning Menggunakan Model Neural Network Pada Dataset Movielens,” Jurnal Locus Penelitian dan Pengabdian, vol. 4, no. 1, pp. 1047–1054, Jan. 2025, doi: 10.58344/locus.v4i1.3768.
Y. Ali et al., “A hybrid group-based movie recommendation framework with overlapping memberships,” PLoS One, vol. 17, no. 3 March, Mar. 2022, doi: 10.1371/journal.pone.0266103.
M. Vahidi Farashah, A. Etebarian, R. Azmi, and R. Ebrahimzadeh Dastjerdi, “A hybrid recommender system based-on link prediction for movie baskets analysis,” J Big Data, vol. 8, no. 1, Dec. 2021, doi: 10.1186/s40537-021-00422-0.
T. L. Ho, A. C. Le, and D. H. Vu, “Multiview Fusion Using Transformer Model for Recommender Systems: Integrating the Utility Matrix and Textual Sources,” Applied Sciences (Switzerland), vol. 13, no. 10, May 2023, doi: 10.3390/app13106324.
S. Bohra and A. Gaikwad, “Enhancing Movie Recommendation using Ensemble based Machine Learning Approach,” International Journal of Engineering Trends and Technology, vol. 72, no. 10 October, pp. 191–203, Oct. 2024, doi: 10.14445/22315381/IJETT-V72I10P119.
S. Jayalakshmi, N. Ganesh, R. Čep, and J. S. Murugan, “Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions,” Sensors, vol. 22, no. 13, Jul. 2022, doi: 10.3390/s22134904.
H. Ma et al., “Negative Sampling in Recommendation: A Survey and Future Directions,” Sep. 2024, [Online]. Available: http://arxiv.org/abs/2409.07237
D. E. Cahyani and I. Patasik, “Performance comparison of tf-idf and word2vec models for emotion text classification,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 5, pp. 2780–2788, Oct. 2021, doi: 10.11591/eei.v10i5.3157.
M. Ibrahim, I. S. Bajwa, N. Sarwar, H. A. Waheed, M. Z. Hasan, and M. Z. Hussain, “Improved Hybrid Deep Collaborative Filtering Approach for True Recommendations,” Computers, Materials and Continua, vol. 74, no. 3, pp. 5301–5317, 2023, doi: 10.32604/cmc.2023.032856.
M. B. Aji and F. Idifitriani, “Terbit online pada laman web jurnal: https://ejurnalunsam.id/index.php/jicom/ Analisis Perbandingan RMSE Algoritma Machine Learning dalam Memprediksi Harga Saham”, [Online]. Available: https://ejurnalunsam.id/index.php/jicom/
