Fashion Recommendation System using Collaborative Filtering


  • Muhammad Khiyarus Syiam Telkom University, Bandung, Indonesia
  • Agung Toto Wibowo * Mail Telkom University, Bandung, Indonesia
  • Erwin Budi Setiawan Telkom University, Bandung, Indonesia
  • (*) Corresponding Author
Keywords: Collaborative Filtering; Cosine Similarity; Recommendation System; Item-based; Fashion

Abstract

Collaborative Filtering is an method used to build a recommendation system with the concept that conclusions from different clients are used to anticipate things that may be of interest to users. This research uses data from Rent the Runway and the method used is Item-based Collaborative filtering, where the system will look for similarities in products that have been purchased by customers and then look for predictive values. Fashion requires recommendations because it plays a crucial role in helping individuals express their identity, personal style, and personality through clothing choices, accessories, and dressing styles.The recommendation system uses the item method based on analyzing the number of purchases or sales and grouping according to each product category so that it can help consumers in choosing fashion products. It was found that the use of Adjusted Cosine Similarity produces better recommendations with an average MAE value of 0.2750, while Cosine Similarity with an average MAE difference of 0.3989. This proves that the use of adjusted cosine similarity can produce better recommendations because the adjustment algorithm not only considers user behavior, but also produces lower performance errors.

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References

H. Wang, M. Zhao, X. Xie, W. Li, and M. Guo, “Knowledge graph convolutional networks for recommender systems,” Web Conf. 2019 - Proc. World Wide Web Conf. WWW 2019, pp. 3307–3313, 2019, doi: 10.1145/3308558.3313417.

M. I. Rizky, I. Asror, and Y. R. Murti, “Sistem Rekomendasi Program Studi untuk Siswa SMA Sederajat Menggunakan Metode Hybrid Recommendation dengan Content Based Filtering dan Collaborative Filtering,” e-proceeding Eng., vol. 7, no. 1, pp. 2776–2792, 2020.

A. H. Ritdrix and P. W. Wirawan, “Sistem Rekomendasi Buku Menggunakan Metode Item-Based Collaborative Filtering,” J. Masy. Inform., vol. 9, no. 2, pp. 24–32, 2018, doi: 10.14710/jmasif.9.2.31482.

R. S. Wahono, Data Mining, vol. 2, no. January 2013. 2023. [Online]. Available: https://www.cambridge.org/core/product/identifier/CBO9781139058452A007/type/book_part

L. V. Nguyen, M. S. Hong, J. J. Jung, and B. S. Sohn, “Cognitive similarity-based collaborative filtering recommendation system,” Appl. Sci., vol. 10, no. 12, pp. 1–14, 2020, doi: 10.3390/APP10124183.

F. Xue, X. He, X. Wang, J. Xu, K. Liu, and R. Hong, “Deep item-based collaborative filtering for top-N recommendation,” ACM Trans. Inf. Syst., vol. 37, no. 3, 2019, doi: 10.1145/3314578.

M. Singh Kumar and R. Prakas, “EVENT DRIVEN RECOMMENDATION SYSTEM FOR E-COMMERCE USING KNOWLEDGE BASED COLLABORATIVE FILTERING TECHNIQUE,” vol. 301, no. 3, pp. 2014–2016, 2020, doi: 10.12694:/scpe.v21i3.1709.

A. Laksito and M. R. Saputra, “Content Based VGG16 Image Extraction Recommendation,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 3, pp. 370–375, 2022, doi: 10.29207/resti.v6i3.3909.

K. R. Dwi, “Sistem Rekomendasi Komunitas Pemuda Di Kota Semarang Berbasis Item Based Collaborative Filtering Dengan Metode Adjusted Cosine,” Tek. Inform., vol. 3, p. 8, 2018, [Online]. Available: http://eprints.dinus.ac.id/16530/1/jurnal_15496.pdf

A. GUNAWAN, “Implementasi Sistem Rekomendasi Produk Dengan Metode Item-Based Collaborative Filtering Berbasis Algoritma Adjusted Cosine …,” 2022, [Online]. Available: https://repository.mercubuana.ac.id/66701/%0Ahttps://repository.mercubuana.ac.id/66701/2/01 COVER.pdf

X. Wang, H. Jin, A. Zhang, X. He, T. Xu, and T. S. Chua, “Disentangled Graph Collaborative Filtering,” SIGIR 2020 - Proc. 43rd Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., pp. 1001–1010, 2020, doi: 10.1145/3397271.3401137.

I. W. Jepriana and S. Hanief, “Collaborative Filtering Untuk Sistem Rekomendasi Konsentrasi Di Stmik Stikom Bali,” vol. 9, pp. 171–180, 2020.

X. Wang, X. He, M. Wang, F. Feng, and T. S. Chua, “Neural graph collaborative filtering,” SIGIR 2019 - Proc. 42nd Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., pp. 165–174, 2019, doi: 10.1145/3331184.3331267.

Anderias Eko Wijaya and Deni Alfian, “Sistem Rekomendasi Laptop Menggunakan Collaborative Filtering Dan Content-Based Filtering,” J. Comput. Bisnis, vol. 12, no. 1, pp. 11–27, 2018.

S. Saha et al., “Feature selection for facial emotion recognition using cosine similarity-based harmony search algorithm,” Appl. Sci., vol. 10, no. 8, pp. 1–22, 2020, doi: 10.3390/APP10082816.

S. C. Mana and T. Sasipraba, “Research on cosine similarity and pearson correlation based recommendation models,” J. Phys. Conf. Ser., vol. 1770, no. 1, 2021, doi: 10.1088/1742-6596/1770/1/012014.

J. Yang, Y. Li, W. Cheng, Y. Liu, and C. Liu, “EKF-GPR-Based fingerprint renovation for subset-based indoor localization with adjusted cosine similarity,” Sensors (Switzerland), vol. 18, no. 1, 2018, doi: 10.3390/s18010318.

Z. Wang et al., “NeuralCubes: Deep Representations for Visual Data Exploration,” Proc. - 2021 IEEE Int. Conf. Big Data, Big Data 2021, pp. 550–561, 2021, doi: 10.1109/BigData52589.2021.9671390.

R. Langner, T. Horak, and R. Dachselt, “Demonstrating vistiles: Visual data exploration using mobile devices,” Proc. Work. Adv. Vis. Interfaces AVI, pp. 1–3, 2018, doi: 10.1145/3206505.3206583.

J. Ni, J. Li, and J. McAuley, “Justifying recommendations using distantly-labeled reviews and fine-grained aspects,” EMNLP-IJCNLP 2019 - 2019 Conf. Empir. Methods Nat. Lang. Process. 9th Int. Jt. Conf. Nat. Lang. Process. Proc. Conf., pp. 188–197, 2019, doi: 10.18653/v1/d19-1018.


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Article History
Submitted: 2023-06-20
Published: 2023-09-27
Abstract View: 470 times
PDF Download: 383 times
How to Cite
Syiam, M., Wibowo, A., & Setiawan, E. (2023). Fashion Recommendation System using Collaborative Filtering. Building of Informatics, Technology and Science (BITS), 5(2), 376−385. https://doi.org/10.47065/bits.v5i2.3690
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