Interpretable Product Recommendation through Association Rule Mining:
An Apriori-Based Analysis on Retail Transaction Data
Dublin Core
Title
Interpretable Product Recommendation through Association Rule Mining:
An Apriori-Based Analysis on Retail Transaction Data
An Apriori-Based Analysis on Retail Transaction Data
Subject
Apriori Algorithm, Association Rule Mining, Interpretable Recommendation, Market Basket Analysis, Retail Analytics, Data-Driven Decision
Making
Making
Description
The rapid growth of e-commerce has generated vast amounts of transactional data, creating opportunities for data-driven decision-making in
retail environments. This study presents an interpretable product recommendation approach based on association rule mining using the Apriori
algorithm. Unlike complex black-box recommender models, the proposed method emphasizes transparency and explainability in identifying
purchasing relationships. The Groceries dataset comprising 38,765 transactions was analyzed to discover frequent itemsets and generate
actionable association rules. After applying minimum thresholds of 0.02 for support and 0.4 for confidence, a total of 67 frequent itemsets and
45 strong rules were obtained. The rule {whole milk, sausage, rolls/buns} → {yogurt} achieved the highest lift value of 1.66, revealing meaningful
co-purchasing behavior. Visualization tools, including heatmaps and network graphs, were employed to illustrate rule strength and product
interconnections, facilitating business interpretation. The findings demonstrate that interpretable rule-based recommendations can effectively
support product bundling, cross-selling, and retail layout strategies. This study highlights the continuing relevance of Apriori in creating
transparent, data-driven insights and proposes future integration with hybrid models to address personalization and scalability challenges in
modern recommendation systems
retail environments. This study presents an interpretable product recommendation approach based on association rule mining using the Apriori
algorithm. Unlike complex black-box recommender models, the proposed method emphasizes transparency and explainability in identifying
purchasing relationships. The Groceries dataset comprising 38,765 transactions was analyzed to discover frequent itemsets and generate
actionable association rules. After applying minimum thresholds of 0.02 for support and 0.4 for confidence, a total of 67 frequent itemsets and
45 strong rules were obtained. The rule {whole milk, sausage, rolls/buns} → {yogurt} achieved the highest lift value of 1.66, revealing meaningful
co-purchasing behavior. Visualization tools, including heatmaps and network graphs, were employed to illustrate rule strength and product
interconnections, facilitating business interpretation. The findings demonstrate that interpretable rule-based recommendations can effectively
support product bundling, cross-selling, and retail layout strategies. This study highlights the continuing relevance of Apriori in creating
transparent, data-driven insights and proposes future integration with hybrid models to address personalization and scalability challenges in
modern recommendation systems
Creator
Agung Budi Prasetio1,*, Burhanuddin bin Mohd Aboobaider2
, Asmala bin Ahmad3
, Asmala bin Ahmad3
Source
https://ijiis.org/index.php/IJIIS/article/view/252/160
Publisher
Universiti Teknikal Malaysia Melaka, Malaysia
Date
march 2025
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Agung Budi Prasetio1,*, Burhanuddin bin Mohd Aboobaider2
, Asmala bin Ahmad3, “Interpretable Product Recommendation through Association Rule Mining:
An Apriori-Based Analysis on Retail Transaction Data,” Repository Horizon University Indonesia, accessed January 1, 2026, https://repository.horizon.ac.id/items/show/9728.
An Apriori-Based Analysis on Retail Transaction Data,” Repository Horizon University Indonesia, accessed January 1, 2026, https://repository.horizon.ac.id/items/show/9728.