Collaborative Filtering Recommendation System Using A Combination of Clustering and Association Rule Mining

Authors

  • Siti Annisa Sriwijaya University, Indonesia
  • Dian Palupi Rini Sriwijaya University, Indonesia
  • Abdiansah Abdiansah Sriwijaya University, Indonesia
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DOI:

https://doi.org/10.51519/journalisi.v6i3.802

Keywords:

Recommendation System, Clustering, Collaborative Filtering, Association Rule Mining

Abstract

A recommendation system helps collect and analyze user data to generate personalized recommendations for users. A recommendation system for movies has been implemented, considering the vast number of available films and the difficulty users face in finding movies that match their interests. One popular recommendation method is Collaborative Filtering (CF). Although widely applied, CF still has issues. Basic CF uses overlapping user data in evaluating items to calculate user similarity. This study aims to build a collaborative filtering recommendation system using clustering techniques to group users with similar interests into the same clusters. The next step in CF application is to gather recommendation candidate items by finding users with a high level of similarity to the target user. Subsequently, user pattern analysis is carried out by applying association rule mining to predict hidden correlations based on frequently watched items and the ratings given to those movies. This study uses rating data and movie data from the Movielens website. The evaluation of the recommendation results is measured using precision, recall, and f-measure. The evaluation results show that the proposed recommendation system achieves a hit rate of 95.08%, a precision of 81.49%, a recall of 98.06%, and an f-measure of 87.66%.

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References

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., vol. 149, 2020, doi: 10.1016/j.eswa.2020.113248.

D. A. Mukhsinin, M. Rafliansyah, and S. A. Ibrahim, “Implementation of Decision Tree Algorithm for Movie Recommendation and Rating Classification on the Netflix Platform Implementasi Algoritma Decision Tree untuk Rekomendasi Film dan Klasifikasi Rating pada Platform Netflix,” vol. 4, no. April, pp. 570–579, 2024.

S. Halder, M. Samiullah, A. M. J. Sarkar, and Y. K. Lee, “Movie swarm: Information mining technique for movie recommendation system,” 2012 7th Int. Conf. Electr. Comput. Eng. ICECE 2012, no. February, pp. 462–465, 2012, doi: 10.1109/ICECE.2012.6471587.

P. S. Sundari and M. Subaji, “An improved hidden behavioral pattern mining approach to enhance the performance of recommendation system in a big data environment,” no. xxxx, 2020.

Y. Lv, Y. Zheng, F. Wei, C. Wang, and C. Wang, “AICF: Attention-based item collaborative filtering,” Adv. Eng. Informatics, vol. 44, no. February, p. 101090, 2020, doi: 10.1016/j.aei.2020.101090.

K. Patel and H. B. Patel, “A state-of-the-art survey on recommendation system and prospective extensions,” Comput. Electron. Agric., vol. 178, no. September, p. 105779, 2020, doi: 10.1016/j.compag.2020.105779.

J. Xu, X. Zheng, and W. Ding, “Personalized recommendation based on reviews and ratings alleviating the sparsity problem of collaborative filtering,” Proc. - 9th IEEE Int. Conf. E-bus. Eng. ICEBE 2012, pp. 9–16, 2012, doi: 10.1109/ICEBE.2012.12.

M. K. Najafabadi, M. N. ri Mahrin, S. Chuprat, and H. M. Sarkan, “Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data,” Comput. Human Behav., vol. 67, pp. 113–128, 2017, doi: 10.1016/j.chb.2016.11.010.

C. Zhang, W. Huang, T. Niu, Z. Liu, G. Li, and D. Cao, “Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems,” Automot. Innov., vol. 6, no. 1, pp. 89–115, 2023, doi: 10.1007/s42154-022-00205-0.

R. Obeidat, R. Duwairi, and A. Al-Aiad, “A Collaborative Recommendation System for Online Courses Recommendations,” Proc. - 2019 Int. Conf. Deep Learn. Mach. Learn. Emerg. Appl. Deep. 2019, pp. 49–54, 2019, doi: 10.1109/Deep-ML.2019.00018.

U. Liji, Y. Chai, and J. Chen, “Improved personalized recommendation based on user attributes clustering and score matrix filling,” Comput. Stand. Interfaces, vol. 57, no. November 2017, pp. 59–67, 2018, doi: 10.1016/j.csi.2017.11.005.

C. F. Tsai and C. Hung, “Cluster ensembles in collaborative filtering recommendation,” Appl. Soft Comput. J., vol. 12, no. 4, pp. 1417–1425, 2012, doi: 10.1016/j.asoc.2011.11.016.

Alith Fajar Muhammad, “Klasterisasi Proses Seleksi Pemain Menggunakan Algoritma K-Means (Study Kasus : Tim Hockey Kabupaten Kendal),” Jur. Tek. Inform. FIK UDINUS, vol. 1, no. 1, pp. 1–5, 2015.

F. O. Isinkaye, Y. O. Folajimi, and B. A. Ojokoh, “Recommendation systems: Principles, methods and evaluation,” Egypt. Informatics J., vol. 16, no. 3, pp. 261–273, 2015, doi: 10.1016/j.eij.2015.06.005.

E. T. L. Kusrini, “Algoritma data mining,” pp. 63–77, 2009.

H. H. Arfisko, F. Informatika, U. Telkom, A. T. Wibowo, F. Informatika, and U. Telkom, “Sistem Rekomendasi Film Menggunakan Metode Hybrid Collaborative Filtering Dan Content-Based Filtering,” vol. 9, no. 3, pp. 2149–2159, 2022.

R. Trihatmaja and Y. D. Wardhana Asnar, “Improving the Performance of Collaborative Filtering Using Outlier Labeling, Clustering, and Association Rule Mining,” Proc. 2018 5th Int. Conf. Data Softw. Eng. ICoDSE 2018, pp. 1–6, 2018, doi: 10.1109/ICODSE.2018.8705883.

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Published

2024-09-12

Issue

Vol. 6 No. 3 (2024): September

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Articles

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How to Cite

[1]
S. Annisa, D. P. Rini, and A. Abdiansah, “Collaborative Filtering Recommendation System Using A Combination of Clustering and Association Rule Mining”, journalisi, vol. 6, no. 3, pp. 1499–1516, Sep. 2024, doi: 10.51519/journalisi.v6i3.802.

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