IMPROVING SHOPPING EXPERIENCES AT NTB MALL THROUGH PERSONALIZED PRODUCT RECOMMENDATIONS USING  CONTENT-BASED FILTERING

Ario Yudo Husodo; Fitri Bimantoro; Nadiyasari Agitha; Nuraqilla Waidha Bintang Grendis

doi:10.52436/1.jutif.2025.6.1.4194

Ario Yudo Husodo Department of Informatics Engineering, University of Mataram, Indonesia
Fitri Bimantoro Department of Informatics Engineering, University of Mataram, Indonesia
Nadiyasari Agitha Department of Informatics Engineering, University of Mataram, Indonesia
Nuraqilla Waidha Bintang Grendis Faculty of Technical and Vocational Education, Universiti Tun Hussein Onn Malaysia Parit Raja, Malaysia

DOI: https://doi.org/10.52436/1.jutif.2025.6.1.4194

Keywords: AI in recommendations, content-based filtering, cosine-similarity, e-commerce personalization, user engagement

Abstract

NTB MALL, an e-commerce platform specializing in unique products from micro, small, and medium enterprises (MSMEs) in West Nusa Tenggara, faces challenges in providing personalized product recommendations due to the diversity of its product categories and consumer preferences. To address this, this study implements a content-based filtering (CBF) approach utilizing Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity to enhance recommendation accuracy. The system analyzes product attributes and user interaction history to generate tailored suggestions. Experimental results indicate that cosine similarity outperforms Euclidean distance in recommendation precision, achieving an accuracy of 89% and a Mean Reciprocal Rank (MRR) of 95%. Furthermore, user feedback reveals that 93% of users found the recommendations highly relevant, 89% reported increased engagement, and 96% expressed satisfaction with the personalized shopping experience. This research provides a novel application of AI-driven recommendation systems in regional e-commerce marketplaces, demonstrating their potential to improve user experience and foster stronger connections between consumers and local producers.

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References

Y. Jin, “An Empirical Study on the Development Environment of Cross-border E-commerce Logistics in the Global Trade Context: Exploring the Digital Marketing Landscape,” Comput. Aided. Des. Appl., vol. 21, no. S4, pp. 290–305, 2024, doi: 10.14733/cadaps.2024.S4.290-305.

R. De Filippis and A. Al Foysal, “The Fusion of Minds: Navigating the Confluence of AI, ML, and Psychology in the Digital Era,” J. Math. Tech. Comput. Math., vol. 3, no. 6, pp. 01–09, 2024, doi: 10.33140/jmtcm.03.06.01.

M. Louis, “Personalized recommendation systems for customer self-service and promotions : Enhancing effortless customer experience,” vol. 11, no. 03, pp. 509–526, 2021.

M. K. M. Swami, “A study on Challenges in the Rural Marketing of FMCG Products,” vol. 20, pp. 267–278, 2024.

S. H. Nallamala, U. R. Bajjuri, S. Anandarao, D. D. D. Prasad, and D. P. Mishra, “A Brief Analysis of Collaborative and Content Based Filtering Algorithms used in Recommender Systems,” IOP Conf. Ser. Mater. Sci. Eng., vol. 981, no. 2, p. 22008, Dec. 2020, doi: 10.1088/1757-899X/981/2/022008.

S.-C. Necula and V.-D. Păvăloaia, “AI-Driven Recommendations: A Systematic Review of the State of the Art in E-Commerce,” Appl. Sci., vol. 13, no. 9, 2023, doi: 10.3390/app13095531.

R. S. Mulyana, A. Id Hadiana, and E. Ramadhan, “Recommendation System Of Product Sales Ideas For MSMEs Using Content-based Filtering and Collaborative Filtering Methods,” in 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), 2023, pp. 252–256. doi: 10.1109/ICCoSITE57641.2023.10127844.

N. Mall, “Tentang NTB Mall,” ntbmall.com.

F. Bimantoro, A. Y. Husodo, N. Agitha, S. E. Anjarwani, and M. A. Albar, “PELATIHAN PENGGUNAAN APLIKASI NTB MALL UNTUK PEDAGANG KAKI LIMA DI KEK MANDALIKA,” vol. 5, no. September, pp. 1–6, 2023.

V. Rolanda, T. S. Gunawan, and Wanayumini, “Content-Based Filtering Recommendation System Using Categories Search Engine,” Int. J. Res. Vocat. Stud., vol. 2, no. 4, pp. 120–125, 2023, doi: 10.53893/ijrvocas.v2i4.177.

E. Robbi, M. Bronzini, P. Viappiani, and A. Passerini, “Personalized bundle recommendation using preference elicitation and the Choquet integral,” Front. Artif. Intell., vol. 7, 2024, doi: 10.3389/frai.2024.1346684.

M. Liao, M. Liao, S. S. Sundar, and S. S. Sundar, “When E-Commerce Personalization Systems Show and Tell: Investigating the Relative Persuasive Appeal of Content-Based versus Collaborative Filtering,” J. Advert., 2021, doi: 10.1080/00913367.2021.1887013.

M. Fahrurrozi, Mispandi, and D. Yunitasari, “INFLUENCE OF DIGITAL ENTREPRENEURS AND CONSUMER BEHAVIOR ON PRODUCT PURCHASING DESICIONS,” J. PROFIT Kaji. Pendidik. Ekon. dan Ilmu Ekon., 2024, doi: 10.36706/jp.v11i2.40.

C. Ji et al., “Pattern-Guided file compression with user-Experience enhancement for log-Structured file system on mobile devices,” USENIX Conf. File Storage Technol., 2021, doi: null.

S. D, N. D, and N. D. H. D, “User Experience of Mobile Banking Application in Indonesia: New Technology of Banking,” Glob. Bus. Financ. Rev., 2024, doi: 10.17549/gbfr.2024.29.2.127.

S. Nuanmeesri, “Development of community tourism enhancement in emerging cities using gamification and adaptive tourism recommendation,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 10, Part A, pp. 8549–8563, 2022, doi: https://doi.org/10.1016/j.jksuci.2021.04.007.

K. Xu, H. Zhou, H. Zheng, M. Zhu, and Q. Xin, “Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning,” Appl. Comput. Eng., 2024, doi: 10.48550/arxiv.2403.19345.

V. Paranjape, S. Sharma, and A. Singh, “Developing a User-Friendly Hotel Recommendation System Based on User Reviews,” Int. J. Innov. Res. Comput. Commun. Eng., 2022, doi: 10.15680/ijircce.2022.1002051.

D. C. Sathyakumar, “Techniques and practices for optimizing resources in large scale horizontal web applications that deliver cross functional UX components,” IEEE Int. Conf. Electro/Information Technol., 2024, doi: 10.1109/eit60633.2024.10609896.

V. Garg and P. Singh, “Optimizing Digital Flyer Experiences with Data Integration for E-commerce,” Integr. J. Res. Arts Humanit., 2024, doi: 10.55544/ijrah.4.6.20.

Y. Yudhanto, W. M. Pryhatyanto, and W. Sulandari, “Designing and Making UI/UX Designs on The Official Website with The Design Thinking Method,” in 2022 1st International Conference on Smart Technology, Applied Informatics, and Engineering (APICS), 2022, pp. 165–170. doi: 10.1109/APICS56469.2022.9918684.

J. Li, J. Li, G. W. Tigwell, G. W. Tigwell, K. Shinohara, and K. Shinohara, “Accessibility of High-Fidelity Prototyping Tools,” Int. Conf. Hum. Factors Comput. Syst., 2021, doi: 10.1145/3411764.3445520.

I. Prazina, Š. Bećirović, E. Cogo, and V. Okanović, “Methods for Automatic Web Page Layout Testing and Analysis: A Review,” IEEE Access, vol. 11, pp. 13948–13964, 2023, doi: 10.1109/ACCESS.2023.3242549.

M. Zielske and T. Held, “Agile methods used by traditional logistics companies and logistics start-ups: a systematic literature review,” J. Syst. Softw., vol. 190, p. 111328, 2022, doi: https://doi.org/10.1016/j.jss.2022.111328.

J. C. Gardey, J. C. Gardey, A. Garrido, A. Garrido, A. Garrido, and A. Garrido, “User Experience Evaluation through Automatic A/B Testing,” IUI Companion, 2020, doi: 10.1145/3379336.3381514.

F. Ji, Q. Cao, H. Li, H. Fujita, C. Liang, and J. Wu, “An online reviews-driven large-scale group decision making approach for evaluating user satisfaction of sharing accommodation,” Expert Syst. Appl., vol. 213, p. 118875, 2023, doi: https://doi.org/10.1016/j.eswa.2022.118875.