The Hybrid Recommender System of the Indonesian  Online Market Products using IMDb weight rating and TF-IDF

Johari, Muhammad; Laksito, Arif Dwi

doi:10.29207/resti.v5i5.3486

Cited by 3 publications

(8 citation statements)

References 16 publications

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“…Setelah berhasil menghitung nilai TF, langkah selanjutnya adalah menghitung nilai Inverse Document Frequency (IDF), yang merupakan nilai untuk mengukur seberapa penting sebuah kata. Pembobotan yang dilakukan IDF adalah dengan cara memberikan nilai tinggi untuk kata dengan frekuensi kemunculan kecil, dan memberikan nilai kecil untuk kata dengan frekuensi kemunculan besar [6]. TF-IDF dapat dihitung menggunakan persamaan (1).…”

Section: Tf-idf Transformationunclassified

“…Untuk menghitung nilai similarity antar pengguna dapat menggunakan Cosine Similarity [1]. Cosine Similarity adalah suatu metode yang umumnya digunakan untuk membandingkan kesamaan dokumen atau memberikan ranking ke dokumen dan dokumen tersebut dianggap sebagai vector [6]. Cosine Similarity dapat dihitung menggunakan persamaan (4).…”

Section: K-nearest Neighbors (Knn)unclassified

“…[1]- [3], [5]. Selain tiga metode tersebut ada metode lain yang disebut dengan Knowledge-based filtering dan Demographic-based filtering [4], [6], [7]. Knowledge-based filtering akan memberikan rekomendasi berdasarkan data informasi bagaimana suatu item dapat memenuhi kebutuhan pengguna.…”

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“…Pengguna dikatakan memiliki kemiripan jika nilai cosine similarity diatas 0.5 atau diatas 50% Langkah kedua adalah pembuatan model sistem rekomendasi untuk memprediksi rating menggunakan persamaan 3. Nilai k yang ditentukan adalah 4. 𝑟( 𝑡𝑜𝑐ℎ𝑖𝑛𝑜.𝑗𝑢𝑎𝑛,𝐴𝑝𝑝𝑙𝑒 𝑖𝑃ℎ𝑜𝑛𝑒 𝑋𝑟) = (0.75×10)+(0.76×8.9)+(0.75×7.9)+(0.99×9 6). …”

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Smartphone Recommendation System Using Model-Based Collaborative Filtering Method

Prayoga¹,

Kusnawi²

2022

J. Tek. Inform. (JUTIF)

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Smartphone are now an importan item that is needed by many people. The rapid development of technology make smartphone companies are competing to release their best smartphones.The many smartphones in online shop cause user to become disoriented about their choice. A recommendation system can help the user in choosing the smartphone that the user likes. In this study, a recommendation system was made using the collaborative filtering method with the K-Nearest Neighbors algorithm and combined with the application of K-Means algorithm to divide the smartphone into several group. The output of collaborative filtering method is that the model can give smartphone rating predictions to user. The prediction results will be used as the basis for giving recommendations to user. The purpose of smartphones groupping is so that the recommendation results are more specific and accurate. The evaluation of the model gets an MAE value is 1.1047 and RMSE value is 1.7579. So it can be concluded that the development of a smartphone recommendation system was successfully implemented.

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Section: Tf-idf Transformationunclassified

Section: K-nearest Neighbors (Knn)unclassified

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Smartphone Recommendation System Using Model-Based Collaborative Filtering Method

Prayoga¹,

Kusnawi²

2022

J. Tek. Inform. (JUTIF)

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“…This also proves that by using the user ratings and IMDB weighted rating formula and the demographic filtering method, top-n items of any category could be found. [5] N. Muthurasu et al uses the Term-Frequency Inverse Document Frequency to provide recommendations according to the user's preferences. Each data record is converted into a vector by using the TF-IDF vectorization algorithm.…”

Section: IIImentioning

confidence: 99%

Personalized Book Recommendation System using TF-IDF and KNN Hybrid

Rashika¹

2022

IJRASET

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A recommendation system helps an organization to create loyal customers and build trust by offering their desired products and services. These systems today are so powerful that they can handle the new customer too who has visited the site for the first time. With the increasing number of books, people prefer to use e-books. Today, online businesses have emerged that are dedicated only for e-books. They allow their users to purchase any books of their interest or even read them online. This improves their business targets. To make their users engaged, they use machine learning models that recommend users the books based on their preferences. Such a system is called Book Recommendation System. Over the past, a large number of book recommendation systems have been built, most of them are found to be useful for both the organization and the users, and are being put into use in the real world. In this proposed system, we build a Book Recommendation System which recommends a set of books to users based on their previous ratings and readings using content-based filtering, collaborative filtering, and hybrid filtering model. This will save users time in searching the books of their interest.

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Recommender Systems using Hybrid Demographic and Content-Based Filtering methods for UMKM Products

Putri,

Zuraiyah,

Akhmad

2024

JIIKM

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Marketing digitization such as e-commerce is needed by micro, small and medium enterprises (UMKM) in Bogor City and Regency so that the products are more easily accessible to consumers. One of the digital marketing that is commonly used by consumers is an e-commerce website. The Recommendation System is implemented into e-commerce websites to increase consumer convenience in online shopping. The recommendation systems method applied is Demographic Filtering and Content-based Filtering. Demographic Filtering uses IMDB Weighted Rating calculations which generate recommendations globally and give recommendations based on each product's IMDB Weighted score. Content-based Filtering uses Cosine Distance calculations which generate personal recommendations and give recommendations based on the score cosine distance of each product in the form of a presentation of the similarity of products that have been purchased with other products. This research uses 107 UMKM fashion and craft product data that was obtained from Bogor City Regional Craft Center which sells various kinds of UMKM products from Bogor City and Regency. Data preprocessing is then carried out on the raw data, with the Data Cleaning, Data Transforming and Data Splitting stages which divide the data in a ratio of 80:20. The accuracy of Demographic Filtering Recommendation System reaches 82.7% and Content-based Filtering Recommendation System reaches 100%.

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The Hybrid Recommender System of the Indonesian Online Market Products using IMDb weight rating and TF-IDF

Cited by 3 publications

References 16 publications

Smartphone Recommendation System Using Model-Based Collaborative Filtering Method

Smartphone Recommendation System Using Model-Based Collaborative Filtering Method

Personalized Book Recommendation System using TF-IDF and KNN Hybrid

Recommender Systems using Hybrid Demographic and Content-Based Filtering methods for UMKM Products

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