Profit Association Rule Mining with Inventory Measures

Singh, Satpal; Badhe, Vivek

doi:10.1109/cicn.2015.187

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…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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Section: Introductionmentioning

confidence: 99%

Market Basket Analysis with Statistically Improved Association Rules Considering Product Details

Doğan

2023

Preprint

View full text Add to dashboard Cite

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A Recommendation System in E-Commerce with Profit-Support Fuzzy Association Rule Mining (P-FARM)

Doğan

2023

JTAER

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E-commerce is snowballing with advancements in technology, and as a result, understanding complex transactional data has become increasingly important. To keep customers engaged, e-commerce systems need to have practical product recommendations. Some studies have focused on finding the most frequent items to recommend to customers. However, this approach fails to consider profitability, a crucial aspect for companies. From the researcher’s perspective, this study introduces a novel method called Profit-supported Association Rule Mining with Fuzzy Theory (P-FARM), which goes beyond just recommending frequent items and considers a company’s profit while making product suggestions. P-FARM is an advanced data mining technique that creates association rules by finding the most profitable items in frequent item sets. From the practitioners’ standpoints, this method helps companies make better decisions by providing them with more profitable products with fewer rules. The results of this study show that P-FARM can be a powerful tool for improving e-commerce sales and maximizing profit for businesses.

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Profit Association Rule Mining with Inventory Measures

Cited by 2 publications

References 10 publications

Market Basket Analysis with Statistically Improved Association Rules Considering Product Details

Market Basket Analysis with Statistically Improved Association Rules Considering Product Details

A Recommendation System in E-Commerce with Profit-Support Fuzzy Association Rule Mining (P-FARM)

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