Hotel Content-Based Recommendation System

Wahyudi, Kristian; Latupapua, Johanes; Chandra, Ritchie; Girsang, Abba Suganda

doi:10.1088/1742-6596/1485/1/012017

Cited by 11 publications

(9 citation statements)

References 6 publications

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“…Tahap ini memberikan gambaran yang komprehensif tentang hasil analisis dan metode rekomendasi yang telah dilakukan. Data-data dan informasi yang diperoleh dari analisis ini dapat digunakan untuk mengembangkan sistem rekomendasi yang lebih baik dan memberikan pengalaman yang lebih baik kepada pengguna [15].…”

Section: Gambar 7 Hasil Akhir Pra-pemrosesan Dataunclassified

Analyzing the Effectiveness of Collaborative Filtering and Content-Based Filtering Methods in Anime Recommendation Systems

Putri,

Faisal

2023

JKKI

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In the current digital era where content consumption via streaming platforms is increasing, the need for accurate recommendation systems is becoming increasingly important, especially in the animation industry. This research focuses on implementing a recommendation system that can help viewers easily navigate the abundance of content. By comparing collaborative filtering and content-based filtering methods, this research attempts to find the optimal approach for providing anime recommendations. From the results of A/B testing and further analysis, it was found that Collaborative Filtering was effective in providing recommendations based on similar interests between users. On the other hand, content-based filtering offers the advantage of personalizing recommendations based on content characteristics. Additionally, integrating these techniques into mobile applications will enrich the user experience, allowing them to receive recommendations more quickly and interactively. With these findings, this research contributes to the development of more intuitive and responsive recommendation systems, driving the growth of the anime streaming industry by increasing user satisfaction and retention.

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Section: Gambar 7 Hasil Akhir Pra-pemrosesan Dataunclassified

Analyzing the Effectiveness of Collaborative Filtering and Content-Based Filtering Methods in Anime Recommendation Systems

Putri,

Faisal

2023

JKKI

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“…Faktanya jumlah dan variasi informasi yang beredar di internet sangatlah banyak dari segi layanan dan fasilitas bahkan harga penginapan sehingga memberikan masalah kepada pengguna dalam menentukan pilihan yang sesuai dengan preferensi [9]. Untuk mengatasi permasalahan tersebut menggunakan sistem rekomendasi, yaitu suatu teknik yang bisa menyediakan saran/pilihan sesuai dengan preferensi pengguna.…”

Section: Permasalahanunclassified

Membangun Sistem Rekomendasi Hotel dengan Content Based Filtering Menggunakan K-Nearest Neighbor dan Haversine Formula

Muliawan

Badriyah²,

Syarif³

2022

TMJ

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Peningkatakan pertumbuhan industri hotel pada tiap tahunnya dan preferensi konsumen yang bervariasi dalam kebutuhan layanan hotel mengakibatkan konsumen lebih konsumtif dalam memilih hotel. Kurangnya pilihan kriteria bobot pada penyedia layanan hotel mengakibatkan konsumen mengalami kesulitan dalam memilih hotel yang sesuai dengan preferensinya, sehingga diperlukan sebuah sistem rekomendasi hotel sebagai pilihan alternatif dalam memilih hotel. Dalam penelitian ini digunakan permodelan Case Based Reasoning (CBR) untuk memberikan pembelajaran kepada sistem. Pilihan dari user pada pilihan hotel secara otomatis akan disimpan ke dalam database dan dijadikan sebagai data training sehingga sistem akan mendapatkan informasi secara berkelanjutan. Pada penelitian ini diberikan tiga jenis kebutuhan antara lain Kebutuhan Prioritas (KP), Kebutuhan Umum (KU) dan Kebutuhan Tambahan (KT) dan atribut yang digunakan terdapat enam yaitu: fasilitas, lokasi, harga, tipe kamar, bintang dan skor yang sangat mempegaruhi hasil rekomendasi. Untuk setiap nilai bobot yang ada, dilakukan uji validitas bobot kepentingan menggunakan pairwise comparison matrix (PCM) sehingga nilai bobot menjadi valid dengan rentang nilai 0-1. Selain itu penerapan content based filtering menggunakan metode haversine formula dan K-Nearest Neighbor (KNN) dalam menentukan nilai terdekat dengan data training. Dari eksperimen, didapatkan hasil pengukuran performansi yang memuaskan berupa rata-rata kemiripan (similarity) sebesar 84.50% Kata kunci : Case Based Reasoning, Content Based Filtering, Haversine Formula, K-Nearest

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“…is study investigates the findings of the literature by searching research studies around the globe [14]. Wahyudi et al integrate a lot of knowledge into the content-based recommendation system and build a content-based recommendation process from a new perspective [15]. e proposed algorithm is used to recognize the subject of the video on the Internet by convolution of the image recommended by the user [16].…”

Section: Related Workmentioning

confidence: 99%

Designing an English Teaching Resource’s Information Management System Using Collaborative Recommendation

Liu

2022

Mobile Information Systems

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In the modern world, English education institutions are more concerned about how to recommend the required resources to all types of learners and how to improve the application value of the resources. Therefore, this study introduces and combines the collaborative recommendation algorithm to recommend resources to the learners. The development and design of an English teaching resource information management system based on collaborative recommendation technology are in the best interests of English learners. It establishes an English teaching resource information management system to recommend appropriate teaching resources for different English learners and provide personalized recommendations. English teaching resources are used to improve efficiency and make it easier for English learners to find English teaching resources that meet their specific requirements. Firstly, this study briefly introduces the collaborative algorithm and establishes how the score of similar items or users is calculated by the user-based collaborative filtering algorithm and the item-based collaborative filtering algorithm. Based on similarity, the proposed approach recommends the architectural framework of the English teaching system to other users, describes the functions of each module of the system in detail, and detects the system’s application performance through simulation experiments. The result shows that the learning resources are sufficiently accurate. The design system, in comparison with the general information management system, can reduce the time required for users to obtain English teaching resources.

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Hotel Content-Based Recommendation System

Cited by 11 publications

References 6 publications

Analyzing the Effectiveness of Collaborative Filtering and Content-Based Filtering Methods in Anime Recommendation Systems

Analyzing the Effectiveness of Collaborative Filtering and Content-Based Filtering Methods in Anime Recommendation Systems

Membangun Sistem Rekomendasi Hotel dengan Content Based Filtering Menggunakan K-Nearest Neighbor dan Haversine Formula

Designing an English Teaching Resource’s Information Management System Using Collaborative Recommendation

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