Online Purchasing Analytics and Recommendation System Using K-Medoids Clustering

Abstract: The rise of online shopping has transformed consumer behavior, generating large volumes of data related to preferences, purchases, and browsing activities. Businesses now rely heavily on analytics and recommendation systems to improve customer satisfaction, drive sales, and deliver personalized shopping experiences. Among different recommendation techniques, clustering methods like K-Medoids have gained attention for their reliability in grouping users and products. Unlike K-Means, K-Medoids uses actual data points as cluster centers, making it less sensitive to outliers and more stable in identifying purchasing patterns.

The proposed Online Purchasing Analytics and Recommendation System using K-Medoids clustering was designed to analyze user behavior, identify trends, and generate accurate recommendations. Its scope includes collecting and processing purchase history, browsing activities, product details, and demographics. A web-based dashboard was developed to visualize analytics and user clusters, while a recommendation engine provides personalized suggestions. The system, however, is limited to offline model training and excludes deep learning, advanced NLP, or real-time data integration. Experimental research was employed to measure the system’s effectiveness, supported by ISO 25010 quality model-based evaluations. Both qualitative and quantitative tools, including Likert scale questionnaires, ensured systematic, reliable, and meaningful results on attributes like usability, reliability, and functional suitability.

The development process followed Agile (SCRUM) methodology, where tasks such as data preprocessing, feature extraction, clustering, and recommendation logic were iteratively built and refined. Regular sprint reviews and team collaboration ensured flexibility and continuous improvement. For pilot testing, Barangay 432 in Sampaloc, Manila, with 2,136 residents, was selected due to its active online shopping culture. Out of this population, 1,856 participants tested the system, alongside businesses, IT students, and faculty members, bringing the total respondents to 1,976. Evaluation results showed high usability (4.13), functional suitability (4.11), and security (4.07), while reliability (3.96) and efficiency (3.94) also performed positively. The overall weighted mean of 4.04 confirmed that users agreed the system met quality expectations, validating its readiness for real-world deployment.

In conclusion, the study demonstrates that K-Medoids-based recommendation systems significantly enhance online retail by providing accurate, personalized, and meaningful product suggestions. Retail Insight not only improves user satisfaction and shopping efficiency but also supports better stock management and eco-friendly product promotion. The study recommends expanding the system with hybrid recommendation models, dynamic dashboards, real-time sentiment analysis, and integration with sustainability databases for even greater personalization and environmental impact tracking.

Online purchasing System, Analytics, Recommendation and K-Medoids Clustering

1. Chen Et Al. (2008), Using Data Mining To Provide Recommendation Service, ACM Digital Library [Google Scholar] [Crossref]

2. Emre Yıldız,Ceyda Güngör Şen, Eyüp Ensar Işık(2023) A Hyper-Personalized Product Recommendation System Focused On Customer Segmentation: An Application In The Fashion Retail Industry, Journal Of Theoretical And Applied Electronic Commerce Research [Google Scholar] [Crossref]

3. Jiangzhou Deng, Junpeng Guo, Yong Wang (2019), A Novel K-Medoids Clustering Recommendation Algorithm Based On Probability Distribution For Collaborative Filtering, Knowledge-Based Systems (Elservier) [Google Scholar] [Crossref]

4. Kiran Kumar & Rajesh Daruvuri(2025), Enhancing Online Retail Insights: K-Means Clustering And PCA For Customer Segmentation [Google Scholar] [Crossref]

5. Pierfrancesco Bellini, Luciano Alessandro Ipsaro Palesi, Paolo Nesi, Gianni Pantaleo(2022 )Multi Clustering Recommendation System For Fashion Retail, Multimedia Tools And Applications Are Provided Here Courtesy Of Nature Publishing Group [Google Scholar] [Crossref]

6. Salunke & Nichite (2022), Recommender Systems In E-Commerce [Google Scholar] [Crossref]

7. Widiyaningtyas Et Al. (2021), Recommendation Algorithm Using Clustering-Based User Behavior Score Similarity, Computers [Google Scholar] [Crossref]

8. Zengyuan Wu, Lingmin Jin, Jiali Zhao, Lizheng Jing, Liang Chen(2022), Research On Segmenting E-Commerce Customer Through An Improved K-Medoids Clustering Algorithm, Computational Intelligence And Neuroscience Wiles Pulication. [Google Scholar] [Crossref]

9. Zhang Et Al. (2024), E-Commerce Recommender System Based On Improved K-Means Clustering Algorithm , Pubmed Central [Google Scholar] [Crossref]

• Competency and Challenges of BTLED-ICT Students in 2D Animation: An Analytical Study
• Slope Stability Assessment: A Case Study of Embankments Along OMU-Aran-Ilorin Road, Nigeria
• Advancements in Precursors, Materials, Deposition Techniques for Thin Film Research in Electronic Devices: A Mini Review
• “Empowering Indian Women through Entrepreneurship: A Study on Kolkata”
• Impact of Mental Mathematics Proficiency on Job Performance Among Seconadry Schools Teachers in Emohua and Port Hacourt City