Deep Learning based Market Basket Analysis using Association Rules

Abstract

Market Basket Analysis (MBA) is a data mining technique assisting retailers in determining the customer's buying habits while making new marketing decisions as the buyer's desire frequently changes with expanding needs; therefore, transactional data is getting large every day. There is a demand to implement Deep Learning (DL) methods to manipulate this rapidly growing data. In previous research, many authors conducted MBA applying DL and association rules (AR) on retail datasets. AR identifies the association between items to find in which order the customer place items in the basket. AR is only used in mining frequently purchased items from retail datasets. There is a gap in classifying these rules and predicting the next basket item using DL on the transactional dataset. This work proposes a framework using AR as a feature selection while applying DL methods for classification and prediction. The experiments were conducted on two datasets, InstaCart and real-life data from Bites Bakers, which operates as a growing store with three branches and 2233 products. The AR classified at 80,20 and 70,30 splits using CNNN, Bi- LSTM, and CNN-BiLSTM. The results considering simulation at both splits show that Bi-LSTM performs with high accuracy, around 0.92 on the InstaCart dataset. In contrast, CNN-BiLSTM performs best at an accuracy of around 0.77 on Bites Bakers dataset.

Optimizing Mobile Operator Services: Uncovering Customer Preferences With Market Basket Analysis

“…This study focuses on Market Basket Analysis (MBA) data using the Apriori algorithm to identify frequently co-occurring mobile operator services. Past efforts have explored similar analyses (Darmaastawan et al, 2020;Ghous et al, 2023;Kayalvily et al, 2020;Pathan et al, 2019;Qisman et al, 2021;Sinha, 2021) demonstrating the potential for promotions in one category to influence the sales of complementary services.…”

“…Where; N represents total number of transactions in a database over a given period; P(X ∩ Y) represents number of transactions where X and Y are transitioned together; P(X) represents total number of transactions where item X is involved (Ghous et al, 2023;Kayalvily et al, 2020;Sinha, 2021;Pathan et al, 2019;Qisman et al, 2021).…”