A content-based recommender system with consideration of repeat purchase behavior

Kuo, R. J.; Cheng, Hong-Ruei

doi:10.1016/j.asoc.2022.109361

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…The content-based filtering strategy is widely used for recommending documents like websites, journals, and media. The content-based filtering technique generates recommendations based on user profiles and features retrieved from the evaluation history content [11], [13]. The user is given recommendations for things that show a strong positive relationship to the highly rated content.…”

Section: ) Content-based Filtering Techniquementioning

confidence: 99%

An e-Learning Recommendation System Framework

Kong,

Haw,

Palanichamy

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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With the emergence of the digital era, the e-learning platform has become an effective tool for obtaining quality e-learning content. However, despite its potential, the true extent of its capabilities has yet to be fully explored. In order to attract users and maximize revenue, e-learning platforms are now expected to provide content tailored to their users' needs and preferences. These recommendations are generated by considering factors such as prior purchases, browsing history, demographic information, and more. By leveraging these advanced technologies, e-learning platforms can enhance the learning experience by providing users with content that is both engaging and relevant to their individual needs and interests. This paper explores the popular Machine Learning (ML) techniques employed in e-learning content recommender platforms. Two machine learning techniques, k-Nearest Neighbour Baseline (KNNBaseline) and Singular Value Decomposition (SVD), are selected and used to accurately forecast customer interests and preferences. By examining the data patterns and user behaviors, these ML techniques provide insights into the most relevant and personalized educational content for individual users, enhancing their learning experience. The item ratings predicted are generated based on the underlying pattern in past ratings of users. The performance of applied approaches was assessed using several evaluation metrics, which include root mean square error and mean absolute error.

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Section: ) Content-based Filtering Techniquementioning

confidence: 99%

An e-Learning Recommendation System Framework

Kong,

Haw,

Palanichamy

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…In this section, some recent papers on recommender systems in e-commerce are cited. Kuo and Cheng [14] proposed a personalized content-based recommender system integrating the architecture of the existing traditional content-based recommender system for e-commerce with the addition of a feedback adjuster. The results showed that the proposed system which is based on a more objective approach can determine customer preferences based on their repeated purchase behavior, thus avoiding subjective judgments.…”

Section: Related Workmentioning

confidence: 99%

Machine learning based recommender system for e-commerce

Loukili

Messaoudi

Ghazi

2023

IJ-AI

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<span>Nowadays, e-commerce is becoming an essential part of business for many reasons, including the simplicity, availability, richness and diversity of products and services, flexibility of payment methods and the convenience of shopping remotely without losing time. These benefits have greatly optimized the lives of users, especially with the technological development of mobile devices and the availability of the Internet anytime and anywhere. Because of their direct impact on the revenue of e-commerce companies, recommender systems are considered a must in this field. Recommender systems detect items that match the customer's needs based on the customer's previous actions and make them appear in an interesting way. Such a customized experience helps to increase customer engagement and purchase rates as the suggested items are tailored to the customer's interests. Therefore, perfecting recommendation systems that allow for more personalized and accurate item recommendations is a major challenge in the e-marketing world. In our study, we succeeded in developing an algorithm to suggest personal recommendations to customers using association rules via the Frequent-Pattern-Growth algorithm. Our technique generated good results with a high average probability of purchasing the next product suggested by the recommendation system.</span>

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“…Hasil penelitiannya adalah meningkatkan kualitas saran dengan mengintegrasikan arsitektur sistem rekomendasi berbasis konten tradisional dengan komponen baru yang disebut feedback adjuster. Komponen ini dirancang untuk membuat umpan balik pelanggan implisit mencerminkan realitas preferensi dengan mempertimbangkan perilaku pembelian berulang mereka [6]. III.…”

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Pemodelan Sistem Rekomendasi Restoran berdasarkan Preferensi Pengguna dengan Pendekatan Content-Based Filtering

Ilhamsyah,

Rahmayuda,

Midyanti

et al. 2024

JEPIN

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Setiap individu memiliki preferensi makanan yang unik. Karakteristik rasa yang khas dapat memengaruhi seberapa bersedia seseorang membayar untuk hidangan di restoran tertentu. Keterkaitan antara preferensi makanan seseorang dengan karakteristik harga makanan dapat digunakan sebagai faktor penting dalam menentukan rekomendasi restoran. Penelitian ini memodelkan sebuah sistem rekomendasi restoran berdasarkan prefrensi rasa, harga dan rating makan penggunanya sebagai faktor utama dalam mempengaruhi hasil rekomendasi. Analisis data menggunakan 3 atribut yaitu data ulasan restoran, rating dan harga restoran. Teknik scraping dilakukan untuk pengumpulan dataset, adapun jumlah dataset sebanyak 661 data restoran dari hasil scraping. Pengubahan dataset dilakukan dengan proses Pra-Processing yang kemudian dilanjutkan dengan mempelajari model data dengan pendekatan Content-Based Filtering (CBF). Hasil penelitian menunjukkan bahwa rata-rata akurasi yang diberikan sistem rekomendasi yang dibangun adalah 73.33% dari rekomendasi restoran berdasarkan harga dan ulasan.

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A content-based recommender system with consideration of repeat purchase behavior

Cited by 6 publications

References 16 publications

An e-Learning Recommendation System Framework

An e-Learning Recommendation System Framework

Machine learning based recommender system for e-commerce

Pemodelan Sistem Rekomendasi Restoran berdasarkan Preferensi Pengguna dengan Pendekatan Content-Based Filtering

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