Product Recommendation for E-Commerce Data Using Association Rule and Apriori Algorithm

Bandyopadhyay, Soma; Thakur, S. S.; Mandal, Jyotsna Kumar

doi:10.1007/978-3-319-74808-5_51

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…The results show that this model works more efficiently and has higher accuracy than logic or AR mining used standalone. (Bandyopadhyay, Thakur, & Mandal, 2017) proposed a priori algorithm-product recommendation framework for recommending products to users based on the relationships between the products that exist in the users' sessions. The results showed that 60% of the recommended rules are correct.…”

Section: Literature Reviewmentioning

confidence: 99%

Web Mining Techniques - A Framework to Enhance Customer Retention

Ouf

Helmy

Ashraf

2023

International Journal of E-Collaboration

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In e-commerce, retaining customers on the web is a difficult task that requires a good understanding of customers' behavior to be able to predict their needs and interests. Web usage mining (WUM), which is the application of data mining techniques to improve business, helps in understanding customers' behavior on the web. Therefore, this paper proposes and implements a framework to enhance the quality of customer recommendations. Providing customers with what they are looking for helps increase their satisfaction, which will lead to improved retention with the company. The proposed framework was tested and evaluated. The result of testing the proposed framework illustrates that the recommendations based on merged techniques (like clustering, classification, association, and sequential discovery) achieve strong accuracy with a precision value of 74%, coverage of 100%, and an average overall efficiency of F-measure of 86%. which means that the merged technique outperformed each technique and attained much higher overall coverage.

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Section: Literature Reviewmentioning

confidence: 99%

Web Mining Techniques - A Framework to Enhance Customer Retention

Ouf

Helmy

Ashraf

2023

International Journal of E-Collaboration

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“…Promosi yang tepat akan memengaruhi keberlanjutan jangka panjang dari penjualan. Dengan mengetahui pola pembelian pelanggan, manajemen dapat menentukan strategi penjualan terkait dengan promosi, prediksi persediaan produk, dan kombinasi produk yang akan dijual [4] [5].…”

Section: Pendahuluanunclassified

Penerapan Algoritma Apriori dan Fuzzy Tsukamoto untuk Rekomendasi Jumlah Pembelian Barang dan Promo pada Toko Serba Ada

Adithama¹,

Dewi

Hariyadi

2020

Jurnal Informatika

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Persaingan bisnis yang semakin ketat mengharuskan para pemilik bisnis untuk berpikir kritis untuk menjaga kepuasan pelanggan dan meningkatkan penjualan produk. Toko Surya Baru adalah toko serba ada (toserba) yang menjual kebutuhan sehari-hari. Pencatatan persediaan barang dan penjualan masih dilakukan secara manual, serta belum ada analisis terhadap data penjualan secara menyeluruh. Hal ini menyebabkan ada barang yang menumpuk karena kurang laku dan ada barang yang kosong. Masalah lain yang timbul adalah pemberian promo bagi para pelanggan. Pemilik toko kesulitan dalam menganalisis barang apa saja yang laku terjual dengan cepat dan sulit menganalisis barang apa saja yang dibeli bersamaan dalam sekali transaksi. Sistem yang dibangun untuk menangani proses transaksi, dilengkapi dengan sistem rekomendasi jumlah pembelian barang menggunakan Fuzzy Tsukamoto dan pemberian promo menggunakan algoritma Apriori. Dengan mengetahui barang apa saja yang dibeli secara bersamaan, sistem memberikan rekomendasi promo yang sesuai dengan kebutuhan pelanggan. Sistem ini telah berhasil dibangun dan menghasilkan prediksi yang optimal, sehingga membantu pihak manajemen dalam mengambil keputusan.

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“…In [14] The proposed study analyzes content-based recommendations for the E-commerce site, where association rule mining and Apriori algorithm are used for product prediction and recommendation.…”

Section: Crm (Customer Relationship Managementmentioning

confidence: 99%

Türk Perakende Şirketindeki Çevirimiçi Alış Verişler için İlişkililik Kurallarını Çıkarılması: Durum Çalışması

Sivri¹,

Kasapbaşi

2019

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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The extracting association rules of inter-user-product relations used by companies in decision-making processes have been popular for some time, especially for market basket analysis. In this study it is aimed to discover association rules from original online store transaction of a Turkish retail company, in order to help administrator and decision maker also Customer Relationship Management department to initiate campaigns. The main objective is to find out which product item sets are bought together. In order to better compare the results the data are analyzed with and without clustering according to range of ages and gender. Data mining Association analysis methods such as Apriori Algorithm, FP-Growth (Frequent Pattern) then applied which are used to extract association rules. Moreover some of the collaborative filtering metrics namely Jaccard, Pearson, and Cosine function are used to understand the association between products to obtain a recommendation system. The proposed recommendation methods successfully recommended the associated product for the obtained original dataset as high as %65 accuracy. Obtained association rules are shared with the marketing department to initiate and direct forthcoming marketing campaigns.

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Product Recommendation for E-Commerce Data Using Association Rule and Apriori Algorithm

Cited by 6 publications

References 10 publications

Web Mining Techniques - A Framework to Enhance Customer Retention

Web Mining Techniques - A Framework to Enhance Customer Retention

Penerapan Algoritma Apriori dan Fuzzy Tsukamoto untuk Rekomendasi Jumlah Pembelian Barang dan Promo pada Toko Serba Ada

Türk Perakende Şirketindeki Çevirimiçi Alış Verişler için İlişkililik Kurallarını Çıkarılması: Durum Çalışması

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