Sistem Rekomendasi Board-games Berbasis Collaborative Filtering Menggunakan Matrix Factorization
Abstract
Sistem rekomendasi telah menjadi komponen
penting dalam berbagai platform digital saat ini, terutama
dalam membantu pengguna menemukan konten yang sesuai
dengan preferensi mereka. Salah satu pendekatan yang paling
sering digunakan adalah collaborative filtering karena mampu
memprediksi item yang mungkin disukai pengguna. Pada
penelitian ini bertujuan untuk membangun sistem rekomendasi
dengan menggunakan tiga model algoritma matrix factorization,
yaitu Singular Value Decomposition (SVD), SVD++, dan Nonnegative Matrix Factorization (NMF), dengan dataset yang
digunakan dari situs BoardGameGeek(BGG). Penelitian ini
melakukan pengujian dengan menggunakan cross validate
dengan skema pengujian 5 fold. Evaluasi kinerja performansi
dilakukan menggunakan metrik Root Mean Squared Error
(RMSE) dan Mean Absolute Error (MAE). Berdasarkan hasil
pengujian, didapatkan nilai rata-rata terbaik RMSE 1.1809
MAE 0.8801 pada algoritma SVD.
Kata kunci— Sistem Rekomendasi, Collaborative Filtering,
Matrix Factorization, Cross Validation, BoardGameGeek
References
Ion, M., Sacharidis, D., & Werthner, H. (2020). Designing a
Recommender System for Board Games. Proceedings of the
th ACM/SIGAPP Symposium on Applied Computing, 1481–
Kim, J., Wi, J., Jang, S., & Kim, Y. (2020). Sequential
Recommendations on Board-Game Platforms. Symmetry, 12(2),
Aciar, S. V., Fabregat, R., Jové, T., & Aciar, G. (2021).
Enhancing Recommender System with Collaborative Filtering
and User Experiences Filtering. Applied Sciences, 11(24),
, doi.org/10.3390/app112411890
J. Zalewski, M. Ganzha and M. Paprzycki, "Recommender
system for board games," 2019 23rd International Conference
on System Theory, Control and Computing (ICSTCC), Sinaia,
Romania, 2019, pp. 249-254, doi:
1109/ICSTCC.2019.8885455.
Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep Learning
Based Recommender System: A Survey and New Perspectives.
ACM Computing Surveys, 52(1), 1–38.
Mohammed Fadhel Aljunid, Manjaiah D.H., "An Efficient Deep
Learning Approach for Collaborative Filtering Recommender
System," Procedia Computer Science, vol. 171, pp. 829–836,
Ko, H., Lee, S., Park, Y., & Choi, A. (2022). A Survey of
Recommendation Systems: Recommendation Models,
Techniques, and Application Fields. Electronics, 11(1), 141,
doi.org/10.3390/electronics11010141
M. Fu, H. Qu, Z. Yi, L. Lu and Y. Liu, "A Novel Deep LearningBased Collaborative Filtering Model for Recommendation
System," in IEEE Transactions on Cybernetics, vol. 49, no. 3,
pp. 1084-1096, March 2019, doi:
1109/TCYB.2018.2795041.
Koren, Y., Rendle, S., Bell, R. (2022). Advances in
Collaborative Filtering. In: Ricci, F., Rokach, L., Shapira, B.
(eds) Recommender Systems Handbook. Springer, New York,
NY. https://doi.org/10.1007/978-1-0716-2197-4_3
I. Saifudin dan T. Widiyaningtyas, “Systematic Literature
Review on Recommender System: Approach, Problem,
Evaluation Techniques, Datasets,” IEEE Access, vol. 12, pp.
–19837, 2024. doi: 10.1109/ACCESS.2024.3359274.
Duan, R., & Jiang, C. (2022). Combining review-based
collaborative filtering and matrix factorization: A solution to
rating's sparsity problem. Decision Support Systems, 158,
https://doi.org/10.1016/j.dss.2022.113748
R. Mu, "A Survey of Recommender Systems Based on Deep
Learning," in IEEE Access, vol. 6, pp. 69009-69022, 2018, doi:
1109/ACCESS.2018.2880197.
Luekhong, P., & Chansanam, W. (2023). Advancing Book
Recommendation Systems: A Comparative Analysis of
Collaborative Filtering and Matrix Factorization Algorithms.
Journal of Information Systems Engineering and Management.
Duan, R., Jiang, C., & Jain, H. K. (2022). Combining reviewbased collaborative filtering and matrix factorization: A solution
to rating's sparsity problem. Decision Support Systems, 156,
https://doi.org/10.1016/j.dss.2022.113748
R. Barathy and P. Chitra, "Applying Matrix Factorization In
Collaborative Filtering Recommender Systems," 2020 6th
International Conference on Advanced Computing and
Communication Systems (ICACCS), Coimbatore, India, 2020,
pp. 635-639, doi: 10.1109/ICACCS48705.2020.9074227.
S. Jiang, J. Li and W. Zhou, "An Application of SVD++ Method
in Collaborative Filtering," 2020 17th International Computer
Conference on Wavelet Active Media Technology and
Information Processing (ICCWAMTIP), Chengdu, China, 2020,
pp. 192-197, doi: 10.1109/ICCWAMTIP51612.2020.9317347.
Isinkaye, F. O. (2021). Matrix Factorization in Recommender
Systems: Algorithms, Applications, and Peculiar Challenges.
IETE Journal of Research, 69(9), 6087–6100.
Wang, S., Sun, G., & Li, Y. (2020). SVD++ Recommendation
Algorithm Based on Backtracking. Information, 11(7), 369.
https://doi.org/10.3390/info11070369
N. Liu and J. Zhao, "Recommendation System Based on Deep
Sentiment Analysis and Matrix Factorization," in IEEE Access,
vol. 11, pp. 16994-17001, 2023, doi:
1109/ACCESS.2023.3246060.
Jayalakshmi, S., Ganesh, N., Čep, R., & Senthil Murugan, J.
(2022). Movie Recommender Systems: Concepts, Methods,
Challenges, and Future Directions. Sensors, 22(13), 4904.
https://doi.org/10.3390/s22134904
Raghuwanshi, S.K., Pateriya, R.K. (2019). Collaborative
Filtering Techniques in Recommendation Systems. In: Shukla,
R.K., Agrawal, J., Sharma, S., Singh Tomer, G. (eds) Data,
Engineering and Applications. Springer, Singapore.
