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

Fajar Christyawan; Arif Nur Rohman; Anggit Dwi Hartanto

doi:10.51519/journalisi.v6i3.806

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

DOI: 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.

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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.