Application of Content-Based Filtering Method Using Cosine Similarity in Restaurant Selection Recommendation System

Authors

  • Fajar Christyawan Universitas Amikom Yogyakarta, Indonesia
  • Arif Nur Rohman Universitas Amikom Yogyakarta, Indonesia
  • Anggit Dwi Hartanto Universitas Amikom Yogyakarta, Indonesia
Pages Icon

DOI:

https://doi.org/10.51519/journalisi.v6i3.806

Keywords:

Recommendation System, Content-Based Filtering, Restaurant

Abstract

This research focuses on developing a restaurant recommender system designed to assist users in selecting restaurants based on preferences such as cuisine type and proximity, thereby enhancing the dining experience. The system employs a content-based filtering approach combined with the Cosine Similarity algorithm to calculate similarity values between restaurant addresses and categories, ensuring personalized and accurate recommendations. Data for the system was collected from TripAdvisor and Google Maps using a web scraping method, resulting in a comprehensive dataset that reflects a wide variety of dining options. An experiment involving 30 respondents was conducted to evaluate the system's performance under real-world conditions. The results demonstrated an accuracy rate of 88%, indicating that the recommender system effectively delivers highly relevant restaurant suggestions to users. These findings suggest that the system can serve as a valuable tool for culinary tourists and local residents, simplifying the process of discovering new dining experiences and aligning them with individual preferences.

Downloads

Download data is not yet available.

References

S. Kangas, “Collaborative filtering and recommendation systems,” VTT Inf. Technol., pp. 18–20, 2002.

W. Wardiyanta, R. Septiyani, and M. E. S. Rejeki, “Studi Kasus Kualitatif Keberhasilan Restoran Non Waralaba di Yogyakarta,” J. Inov. Penelit., vol. 1, no. 7, pp. 1475–1486, 2020.

D. Bernstein, M. Ottenfeld, and C. L. Witte, “A study of consumer attitudes regarding variability of menu offerings in the context of an upscale seafood restaurant,” J. Foodserv. Bus. Res., vol. 11, no. 4, pp. 398–411, 2008.

M. Alkaff, H. Khatimi, and A. Eriadi, “Sistem Rekomendasi Buku pada Perpustakaan Daerah Provinsi Kalimantan Selatan Menggunakan Metode Content-Based Filtering,” MATRIK J. Manaj. Tek. Inform. Dan Rekayasa Komput., vol. 20, no. 1, pp. 193–202, 2020.

F. Ricci, L. Rokach, and B. Shapira, “Recommender systems: Techniques, applications, and challenges,” Recomm. Syst. Handb., pp. 1–35, 2021.

Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, pp. 30–37, 2009.

D. Jannach, “Finding preferred query relaxations in content-based recommenders,” presented at the 2006 3rd International IEEE Conference Intelligent Systems, IEEE, 2006, pp. 355–360.

R. Burke, “Hybrid recommender systems: Survey and experiments,” User Model. User-Adapt. Interact., vol. 12, pp. 331–370, 2002.

R. F. Oeyliawan and D. Gunawan, “Aplikasi Rekomendasi Buku Pada Katalog Perpustakaan Universitas Multimedia Nusantara Menggunakan Vector Space Model,” Ultim. J. Tek. Inform., vol. 9, no. 2, pp. 97–105, 2017.

R. H. Mondi, A. Wijayanto, and W. Winarno, “Recommendation System With Content-Based Filtering Method for Culinary Tourism in Mangan Application,” ITSMART J. Teknol. Dan Inf., vol. 8, no. 2, pp. 65–72, 2019.

M. Jozani, C. Z. Liu, and K.-K. R. Choo, “An empirical study of content-based recommendation systems in mobile app markets,” Decis. Support Syst., vol. 169, p. 113954, 2023.

M. J. Pazzani, “A framework for collaborative, content-based and demographic filtering,” Artif. Intell. Rev., vol. 13, pp. 393–408, 1999.

M. J. Pazzani and D. Billsus, “Content-based recommendation systems,” in The adaptive web: methods and strategies of web personalization, Springer, 2007, pp. 325–341.

Y. Sun and Y. Zhang, “Conversational recommender system,” presented at the The 41st international acm sigir conference on research & development in information retrieval, 2018, pp. 235–244.

G. V. Tejaswi, S. R. Krishna, G. L. Madhuri, and C. Prasanna, “A Framework to Enhance the Movie Recommendation System by Using Data Mining,” IRE Journals, vol. 5, no. 3, pp. 47-60, 2021.

K. M. A. Ibrahim, “Personalized audio auto-tagging as proxy for contextual music recommendation,” Ph.D. dissertation, Inst. Polytech. Paris., 2021.

R. M. Furr, “A framework for profile similarity: Integrating similarity, normativeness, and distinctiveness,” J. Pers., vol. 76, no. 5, pp. 1267–1316, 2008.

A. A. Huda, R. Fajarudin, and A. Hadinegoro, “Sistem Rekomendasi Content-Based Filtering Menggunakan TF-IDF Vector Similarity Untuk Rekomendasi Artikel Berita,” Build. Inform. Technol. Sci. BITS, vol. 4, no. 3, pp. 1679–1686, 2022.

P. Nastiti, “Penerapan Metode Content Based Filtering Dalam Implementasi Sistem Rekomendasi Tanaman Pangan,” Teknika, vol. 8, no. 1, pp. 1–10, 2019.

R. Faurina and E. Sitanggang, “Implementasi Metode Content-Based Filtering dan Collaborative Filtering pada Sistem Rekomendasi Wisata di Bali.,” Techno Com, vol. 22, no. 4, 2023.

Downloads

Published

2024-09-13

Issue

Vol. 6 No. 3 (2024): September

Section

Articles

License

Authors Declaration

  1. The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
  2. The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
  3. The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
  4. The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
  5. The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
  6. The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
  7. If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
  8. The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights

How to Cite

[1]
F. Christyawan, A. N. Rohman, and A. D. Hartanto, “Application of Content-Based Filtering Method Using Cosine Similarity in Restaurant Selection Recommendation System”, journalisi, vol. 6, no. 3, pp. 1559–1576, Sep. 2024, doi: 10.51519/journalisi.v6i3.806.

Most read articles by the same author(s)