Authors:
Humberto Tozetti Carlos
1
;
Luciana Lee
2
and
Mateus Barcellos Costa
1
Affiliations:
1
Postgraduate Program in Applied Computing (PPComp), Instituto Federal do Espírito Santo, Serra, Brazil
;
2
CEUNES - Universidade Federal do Espírito Santo, São Mateus, Brazil
Keyword(s):
Recommendation Systems, Association Rules, Classification Algorithms, Retail Analytics.
Abstract:
This work presents a study of association and classification algorithms to support sales in retail stores through recommendation systems. The study aimed to evaluate these algorithms in terms of their ability to provide contextual information relevant to sales in retail storefronts. To achieve this goal, two primary objectives were defined. The first was to explore methods for relating sales items. For this approach, experiments were conducted using association rule and clustering algorithms. The second objective was to evaluate the capability of classification algorithms to identify classes of interest present within the data universe. The experiments utilized a dataset from the pharmaceutical sector. In the case of association rule algorithms, given the absence of data to enable recommendations based on collaborative filtering, the purpose was to identify patterns of item associations derived from customer shopping basket data. For the classification algorithms, the goal was to ide
ntify sales with and without medical prescriptions, a fundamental aspect to monitor consumer behavior regarding the use of drugs. For identifying sales with medical prescriptions, the MultiLayer Perceptron algorithm achieved the best results. For predicting items based on the shopping basket, the best results were obtained by combined use of the K-Means, K-Prototype, and FP-Growth algorithms.
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