Advancing Diversity in Recommendation Systems Through Collaborative Filtering: A Focus on Media Content

Authors

  • Chandro Pardede Institut Teknologi Del, Indonesia
  • Parmonangan R. Togatorop Institut Teknologi Del, Indonesia
  • Permana Gabriel Panjaitan Institut Teknologi Del, Indonesia
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DOI:

https://doi.org/10.51519/journalisi.v7i1.1045

Keywords:

Recommendation system, diversity, KNN clustering, item based collaborative filtering

Abstract

A recommendation system provides suggestions based on user preferences, interests, or behavior. However, a major challenge is its tendency to generate monotonous recommendations, reducing diversity and limiting new user experiences. Therefore, increasing diversity is essential to enhance user experience and satisfaction while maintaining recommendation accuracy. This research proposes to apply collaborative filtering method, which focuses on item-based filtering using KNN. This method focuses on item similarity using cosine similarity. To enhance diversity, the system filters results based on similarity and rating thresholds. The evaluation results confirm that applying a similarity threshold increases recommendation diversity, as indicated by consistently higher individual diversity values. Clustering further enhances individual diversity. Findings show that the highest individual diversity with clustering reaches 0.5719, compared to 0.5706 without clustering. These improvements suggest potential applications in domains such as e-commerce and music recommendation systems.

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References

D. Patel, F. Patel, and U. Chauhan, “Recommendation Systems: Types, Applications, and Challenges,” Int. J. Comput. Digit. Syst., vol. 13, no. 1, 2023, doi: 10.12785/ijcds/130168.

Z. Pu and M. A. Beam, “Information Overload and User Satisfaction: Balance Between Reliance on Recommendations and Deliberate News Selection,” in CEUR Workshop Proc., 2021.

L. E. Gomez and P. Bernet, “Diversity improves performance and outcomes,” J. Natl. Med. Assoc., vol. 111, no. 4, 2019, doi: 10.1016/j.jnma.2019.01.006.

P. M. Guest, “Does Board Ethnic Diversity Impact Board Monitoring Outcomes?,” Br. J. Manag., vol. 30, no. 1, 2019, doi: 10.1111/1467-8551.12299.

R. S. Marlianti and S. Saepudin, “Perancangan Enterprise Architecture Sistem Informasi Terminal Menggunakan Model TOGAF ADM,” Teknika, vol. 10, no. 2, 2021, doi: 10.34148/teknika.v10i2.367.

T. Duricic, D. Kowald, E. Lacic, and E. Lex, “Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks,” Front. Data Sci., 2023, doi: 10.3389/fdata.2023.1251072.

L. Zhang et al., “Diversity balancing for two-stage collaborative filtering in recommender systems,” Appl. Sci., vol. 10, no. 4, 2020, doi: 10.3390/app10041257.

R. F. Muttaqien, D. Nurjanah, and H. Nurrahmi, “Diversity Balancing in Two-Stage Collaborative Filtering for Book Recommendation Systems,” J. Tek. Inform., vol. 16, no. 2, 2023, doi: 10.15408/jti.v16i2.36580.

F. W. Ardiyanto, D. Nurjanah, and S. Meliana, “Diversity Balancing pada Two-stage Collaborative Filtering,” e-Proc. Eng., vol. 10, no. 3, 2023.

L. Yankai, “Diversity enhancement for collaborative filtering recommendation,” in CEUR Workshop Proc., 2022.

J. Kim et al., “Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation,” in Lect. Notes Comput. Sci., 2023, doi: 10.1007/978-3-031-33380-4_28.

S. Raza, S. R. Bashir, and U. Naseem, “Accuracy meets Diversity in a News Recommender System,” in Proc. Int. Conf. Comput. Linguist. (COLING), 2022.

R. Roy, V. Rai, and S. R. Kumar, “Movie Recommendation System Using Machine Learning Techniques,” in Proc. 4th Int. Conf. Adv. Comput., Commun. Control Netw. (ICAC3N), 2022, doi: 10.1109/ICAC3N56670.2022.10074205.

M. Goyani and N. Chaurasiya, “A Review of Movie Recommendation System,” Electron. Lett. Comput. Vis. Image Anal., vol. 19, no. 3, 2020, doi: 10.5565/rev/elcvia.1232.

H. Kwon, J. Han, and K. Han, “ART: How the Diversity of Product Recommendations Affects Purchase Preference,” in Proc. Int. Conf. Inf. Knowl. Manag., 2020, doi: 10.1145/3340531.3412687.

A. A. Nurdin and Z. Abidin, “The Influence of Recommendation System Quality on E-commerce Customer Loyalty,” J. Adv. Inf. Syst. Technol., vol. 5, no. 1, 2023, doi: 10.15294/jaist.v5i1.65910.

P. Han et al., “Personalized Re-ranking for Recommendation with Mask Pretraining,” Data Sci. Eng., vol. 8, no. 4, 2023, doi: 10.1007/s41019-023-00219-6.

C. Pei et al., “Personalized re-ranking for recommendation,” in Proc. ACM Conf. Recomm. Syst., 2019, doi: 10.1145/3298689.3347000.

R. Widayanti et al., “Improving Recommender Systems Using Hybrid Techniques,” J. Appl. Data Sci., vol. 4, no. 3, 2023, doi: 10.47738/jads.v4i3.115.

K. P. Sinaga and M. S. Yang, “Unsupervised K-means clustering algorithm,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.2988796.

L. V. Nguyen, Q. T. Vo, and T. H. Nguyen, “Adaptive KNN-Based Extended Collaborative Filtering,” Big Data Cogn. Comput., vol. 7, no. 2, 2023, doi: 10.3390/bdcc7020106.

M. Al-Ghamdi, H. Elazhary, and A. Mojahed, “Evaluation of Collaborative Filtering,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 3, 2021, doi: 10.14569/IJACSA.2021.0120367.

R. H. Singh et al., “Movie Recommendation System using Cosine Similarity and KNN,” Int. J. Eng. Adv. Technol., vol. 9, no. 5, pp. 556–559, Jun. 2020, doi: 10.35940/ijeat.E9666.069520.

S. Alzu’bi, A. Zraiqat, and S. Hendawi, “Sustainable Development: Semantics-aware Movie Recommendation,” Int. J. Adv. Soft Comput. Appl., vol. 14, no. 3, 2022, doi: 10.15849/IJASCA.221128.11.

C. Fiarni and H. Maharani, “Product Recommendation System Design,” Int. J. Inf. Technol. Electr. Eng., vol. 3, no. 2, 2019, doi: 10.22146/ijitee.45538.

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Published

2025-03-24

Issue

Vol. 7 No. 1 (2025): March

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Articles

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How to Cite

[1]
C. Pardede, P. R. Togatorop, and P. G. Panjaitan, “Advancing Diversity in Recommendation Systems Through Collaborative Filtering: A Focus on Media Content”, journalisi, vol. 7, no. 1, pp. 730–745, Mar. 2025, doi: 10.51519/journalisi.v7i1.1045.