An Association Rule Algorithm for Online e-Commerce Recommendation Service

Dai, Jiabei; Zeng, Bin

doi:10.18178/joebm.2016.4.10.454

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…MBA sudah banyak diterapkan pada segmen retail (Kurniawan, Umayah, & Hammad, 2018), penerapan pada aplikasi e-commerce (Dai & Zeng, 2016), implementasi di toko retail perkakas (Sagin & Ayvaz, 2018), implementasi pada supermarket grosir (Musungwini, Zhou, Gumbo, & Mzikamwi, 2014), menggabungkan FP-Growth dengan K-Apriori pada penerapan di supermarket (Annie & Kumar, 2012), menganalisis trend dari pasar (M. Kaur & Kang, 2016) dan meningkatkan penjualan produk (Alfianzah, Handayani, & Murniyati, 2020). Untuk menganalisis pola pembelian pelanggan juga telah diimplementasikan menggunakan Apriori (Panjaitan et al, 2019) dan menggabungkan dengan FP-Growth (Ariestya, Supriyatin, & Astuti, 2019).…”

Section: A Penelitian Terkaitunclassified

Market Basket Analysis for Books Sales Promotion using FP Growth Algorithm, Case Study : Gramedia Matraman Jakarta

Firmansyah¹,

Agus²

2021

JITE

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For retail companies such as Gramedia stores, promotion and strategies to sell books are important, so tools are needed to analyze past sales data. Gramedia does not yet have tools to analyze shopping cart patterns that aim to carry out product promotions appropriately. To promote what books should be promoted using the market basket analysis method or shopping basket analysis. The algorithm used in the data mining process is Frequent Pattern Growth (FP Growth) because it is faster in processing large data. The data analyzed is historical data on book sales from January to March 2020 which is taken randomly (random sampling). The framework used in the data mining process is the Cross Industry Standard Process for Data Mining (CRISP-DM) and the tool used is the Rapid Miner using a market basket analysis framework. With a minimum support of 0.003 and a minimum confidence 0.3 using the FP-Growth algorithm to produce an item set of 7 rules to recommend product promotions. The algorithm results are also in accordance with the business understanding phase of CRISP-DM.

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Section: A Penelitian Terkaitunclassified

Market Basket Analysis for Books Sales Promotion using FP Growth Algorithm, Case Study : Gramedia Matraman Jakarta

Firmansyah¹,

Agus²

2021

JITE

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“…Online shoppers often require direction when selecting the item to buy because of the vast amount of products. This purchasing haze initiates the development of some data-oriented solutions such as product recommendation systems (Dai and Zeng 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Some research focused on developing novel algorithms by benefiting from various methods, such as fuzzy theory (Nagaraj and Mohanraj 2020;Zhang et al 2019a), deep learning (Li et al 2022) and text mining (Verma, Dhalmahapatra, and Maiti 2023;Rohidin, Samsudin, and Deris 2020). On the other hand, some authors expanded the scope of the association rules by adding more perspectives, such as graph sequences (Du and Yu 2022;Wang, Gui, and Xu 2022), profit-based rules (Dai and Zeng 2016;Singh and Badhe 2015). A few research tested the statistical importance of the created rules.…”

Section: Introductionmentioning

confidence: 99%

Market Basket Analysis with Statistically Improved Association Rules Considering Product Details

Doğan

2023

Preprint

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E-commerce companies need to direct customers to choose the wished products. A significant challenge in the e-commerce domain forces to use of technology and data-oriented solutions. So, investigating the sales transactions is necessary thanks to the high number of product varieties for product recommendation. Although creating association rules with many algorithms is common, this study improves the traditional association rule mining (ARM) method by adding a different perspective, named statistical settings, to create more insights for managerial implications. After generating rules, it checks the statistical significance of the correlation, considering some product-specific details, such as product name, discount rates, and the number of favorites. The study concluded with exciting insights. For example, the company should recommend products with a lower discount rate if the customer puts products in the basket with a high discount rate. It also enriches traditional rules by adding product features. When the total number of favorites is less than 7500, and the discount rate is less than 75\%, the similarity ratio of the Conclusion products should be less than 0.50.

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Understanding Machine Learning Through Data-Oriented and Human Learning Approaches

Behpour

Udash²

2022

Educational Communications and Technology: Issues and Innovations

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An Association Rule Algorithm for Online e-Commerce Recommendation Service

Abstract: The focus of his research is in the areas of information management, computer network, industrial engineering and system engineering. He teaches and supervises undergraduate and master students.

Cited by 3 publications

References 11 publications

Market Basket Analysis for Books Sales Promotion using FP Growth Algorithm, Case Study : Gramedia Matraman Jakarta

Market Basket Analysis for Books Sales Promotion using FP Growth Algorithm, Case Study : Gramedia Matraman Jakarta

Market Basket Analysis with Statistically Improved Association Rules Considering Product Details

Understanding Machine Learning Through Data-Oriented and Human Learning Approaches

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