Sistem Rekomendasi Film Menggunakan Data User-End dan Knowledge Graph Convolutional Network pada Dataset MovieLens 1 M

Muhammad Rizki Yanuar; Fajri Rakhmat Umbara; Agus Komarudin -

doi:10.30865/jurikom.v12i4.8772

Authors

Muhammad Rizki Yanuar Universitas Jenderal Achmad Yani
Fajri Rakhmat Umbara Universitas Jenderal Achmad Yani
Agus Komarudin - Universitas Jenderal Achmad Yani

DOI:

https://doi.org/10.30865/jurikom.v12i4.8772

Keywords:

Knowledge Graph, Graph Convolutional Network, Sistem Rekomendasi Film, Importance Sampling, Sparsity

Abstract

Traditional recommendation systems such as Collaborative Filtering and Content-Based Filtering often fail to provide relevant recommendations due to their limitations in handling sparsity and cold-start problems. This study proposes a Knowledge Graph Convolutional Network (KGCN) model enriched with user demographic data from the MovieLens 1M dataset to address these issues. The primary focus of the research is to demonstrate that the Importance Sampling technique is significantly superior to Uniform Sampling in effectively training the model. After hyperparameter tuning, the optimal model configuration achieved peak performance with an AUC score of 0.8798 and NDCG@10 of 0.9719. These results demonstrate that the proposed approach is effective in building an accurate, personalised recommendation system capable of addressing sparsity and cold-start issues.

Author Biographies

Muhammad Rizki Yanuar, Universitas Jenderal Achmad Yani

Sains dan Informatika

Fajri Rakhmat Umbara, Universitas Jenderal Achmad Yani

Sains dan Informatika

Agus Komarudin -, Universitas Jenderal Achmad Yani

Sains dan Informatika

References

Q. Guo et al., “A Survey on Knowledge Graph-Based Recommender Systems,” IEEE Trans Knowl Data Eng, vol. 34, no. 8, pp. 3549–3568, Aug. 2022, doi: 10.1109/TKDE.2020.3028705.

Y. Ji et al., “Accelerating Large-Scale Heterogeneous Interaction Graph Embedding Learning via Importance Sampling,” ACM Trans Knowl Discov Data, vol. 15, no. 1, Jan. 2021, doi: 10.1145/3418684.

H. Wang, M. Zhao, X. Xie, W. Li, and M. Guo, “Knowledge graph convolutional networks for recommender systems,” in The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, Association for Computing Machinery, Inc, May 2019, pp. 3307–3313. doi: 10.1145/3308558.3313417.

T. Gu, H. Liang, C. Bin, and L. Chang, “Combining user-end and item-end knowledge graph learning for personalized recommendation,” Journal of Intelligent and Fuzzy Systems, vol. 40, no. 5, pp. 9213–9225, 2021, doi: 10.3233/JIFS-201635.

J. Chen, T. Ma, and C. Xiao, “FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling,” Jan. 2018, [Online]. Available: http://arxiv.org/abs/1801.10247

X. Wang et al., “Learning intents behind interactions with knowledge graph for recommendation,” in The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021, Association for Computing Machinery, Inc, Apr. 2021, pp. 878–887. doi: 10.1145/3442381.3450133.

X. Zeng, S. Wang, Y. Zhu, M. Xu, and Z. Zou, “A Knowledge Graph Convolutional Networks Method for Countryside Ecological Patterns Recommendation by Mining Geographical Features,” ISPRS Int J Geoinf, vol. 11, no. 12, Dec. 2022, doi: 10.3390/ijgi11120625.

T. Al?shehari and R. A. Alsowail, “An insider data leakage detection using one?hot encoding, synthetic minority oversampling and machine learning techniques,” Entropy, vol. 23, no. 10, Oct. 2021, doi: 10.3390/e23101258.

Intan Permata and Esther Sorta Mauli Nababan, “Application Of Game Theory In Determining Optimum Marketing Strategy In Marketplace,” JURNAL RISET RUMPUN MATEMATIKA DAN ILMU PENGETAHUAN ALAM, vol. 2, no. 2, pp. 65–71, Jul. 2023, doi: 10.55606/jurrimipa.v2i2.1336.

L. Wang and Y. Zhang, “Digital Dissemination of Information Based on Knowledge Graph Recommendation Algorithm,” Informatica (Slovenia), vol. 48, no. 19, pp. 31–44, 2024, doi: 10.31449/inf.v48i19.6561.

J. A. P. Sacenti, R. Fileto, and R. Willrich, “Knowledge graph summarization impacts on movie recommendations,” J Intell Inf Syst, vol. 58, no. 1, pp. 43–66, Feb. 2022, doi: 10.1007/s10844-021-00650-z.

Y. Wang, J. Zeng, Y. Wang, C. Wang, Y. Liu, and X. Wu, “Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding,” ACM Computing Surveys (CSUR), vol. 55, no. 11, pp. 1–36, 2023, doi: 10.1145/3491206.

X. Wang et al., “Learning intents behind interactions with knowledge graph for recommendation,” in The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021, Association for Computing Machinery, Inc, Apr. 2021, pp. 878–887. doi: 10.1145/3442381.3450133.

H. Xia, K. Huang, and Y. Liu, “Unexpected interest recommender system with graph neural network,” Complex and Intelligent Systems, vol. 9, no. 4, pp. 3819–3833, Aug. 2023, doi: 10.1007/s40747-022-00849-9.

C. Li, Y. Cao, Y. Zhu, D. Cheng, C. Li, and Y. Morimoto, “Ripple Knowledge Graph Convolutional Networks for Recommendation Systems,” Machine Intelligence Research, vol. 21, no. 3, pp. 481–494, Jun. 2024, doi: 10.1007/s11633-023-1440-x.

O. Rainio, J. Teuho, and R. Klén, “Evaluation metrics and statistical tests for machine learning,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-56706-x.

S. Siet, S. Peng, S. Ilkhomjon, M. Kang, and D. S. Park, “Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans,” Applied Sciences (Switzerland), vol. 14, no. 6, Mar. 2024, doi: 10.3390/app14062505.

S. Subhan, D. L. Syarif, E. Widhihastuti, S. K. Rakainsa, M. Sam’an, and Y. N. Ifriza, “Improved recommender system using Neural Network Collaborative Filtering (NNCF) for E-commerce cosmetic product,” Sinergi (Indonesia), vol. 29, no. 1, pp. 155–162, 2025, doi: 10.22441/sinergi.2025.1.014.

H. Yang, J. Li, and S. Jahng, “The Application of Knowledge Graph Convolutional Network-based Film and Television Interaction under Artificial Intelligence,” IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3459448.

C. Li, Y. Cao, Y. Zhu, D. Cheng, C. Li, and Y. Morimoto, “Ripple Knowledge Graph Convolutional Networks for Recommendation Systems,” Machine Intelligence Research, vol. 21, no. 3, pp. 481–494, Jun. 2024, doi: 10.1007/s11633-023-1440-x.