Design of an Artificial Intelligence Based Recommendation System to Improve User Experience On E-Commerce Platforms
Abstract
The objective of this research is to explore the design and development of an artificial intelligence-based recommendation system to enhance user experience on e-commerce platforms. The research methodology employed is qualitative research, a method used to gain in-depth understanding of social phenomena. The chosen type of research is a literature review, where in the researcher collects, studies, and analyzes written references or sources such as books, journals, articles, documents, and other significant sources of information related to the researched topic or title. Subsequently, the researcher analyzes and draws conclusions to find answers to the research questions. The research findings indicate that the development of an artificial intelligence-based recommendation system for improving user experience on e-commerce platforms is a relevant and pressing step. By comprehending various aspects discussed in the literature, the researcher can design a system that is not only accurate and efficient but also ethical, secure, and aligned with user needs. For future researchers, further studies are needed, and practical implementation steps will be crucial to translate literature findings into tangible solutions that can enhance the e-commerce landscape in the future.
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Bok, S. K. (2023). Enhancing User Experience in E-Commerce through Personalization Algorithms [fi=AMK-opinnäytetyö|sv=YH-examensarbete|en=Bachelor's thesis|]. http://www.theseus.fi/handle/10024/815645
Cabrera-Sánchez, J.-P., Ramos-de-Luna, I., Carvajal-Trujillo, E., & Villarejo-Ramos, Á. F. (2020). Online Recommendation Systems: Factors Influencing Use in E-Commerce. Sustainability , 12 (21), Article 21. https://doi.org/10.3390/su12218888
Chinchanachokchai, S., Thontirawong, P., & Chinchanachokchai, P. (2021). A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendations. Journal of Retailing and Consumer Services , 61 , 102528. https://doi.org/10.1016/j.jretconser.2021.102528
Fahrurrozi, M. (2023). ENTREPRENEURSHIP & DIGITALIZATION: Developing Business in the 5.0 Era . Hamzanwadi University Press.
Fitriani, FA (2022). Recommendation System for Selection of Skincare Products Using a Content - Based Filtering Approach (Case Study: Review of Skincare Products in Female Daily) [Thesis, Islamic University of Indonesia]. https://dspace.uii.ac.id/handle/123456789/39703
Musdalifah, AA (2023). INNOVATION IN MAKING WEB-BASED COOKING RECIPES AS INFORMATION MEDIA USING USER-CENTERED DESIGN METHODS [Diploma, National University]. http://repository.unas.ac.id/7149/
Mykhalchuk, T., Zatonatska, T., Dluhopolskyi, O., Zhukovska, A., Dluhopolska, T., & Liakhovych, L. (2021). Development of Recommendation System in e-Commerce using Emotional Analysis and Machine Learning Methods. 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) , 1 , 527–535. https://doi.org/10.1109/IDAACS53288.2021.9660854
Nursidik, BP (2023). Use of Chatbots in Implementing User Interface and User Experience for Grocery Stores with a User Centered Design Approach [Bachelor, Siliwangi University]. https://doi.org/10/10.%20DAFTAR%20TABEL.pdf
Peng, N., Xiao, X., Di, W., Ang, L., & Wen, M. (2023). Design and implementation of an intelligent recommendation system for product information on an e-commerce platform based on machine learning. International Conference on Internet of Things and Machine Learning (IoTML 2023) , 12937 , 367–375. https://doi.org/10.1117/12.3013353
Sekarwati, RA, Sururi, A., Rakhmat, R., Arifin, M., & Wibowo, A. (2021). Survey of Chatbot Testing Methods on Social Media to Measure Accuracy. SISFOTENIKA , 11 (2), Article 2. https://doi.org/10.30700/jst.v11i2.1099
Susanto, A., & Purnomo, AS (2022). Design and Build an E-Commerce Application for Helmet Sales Using the Simple Additive Weighting (Saw) Method (Case Study: Jogja Helmet Gallery). Journal of Technology and Business Information Systems , 4 (1), Article 1. https://doi.org/10.47233/jteksis.v4i1.346
Triwidodo, V. (2023). Optimizing GPT Chatbot Features Using the React JS Framework with Smart Canteen Study [Diploma, National University]. http://repository.unas.ac.id/8673/
Valensia, V., & Kurniabudi, K. (2023). Utilization of AR-Based Social Media to Identify the Selection of Glasses Frames at the Idri Glasses Shop. Journal of Information Systems Management , 8 (3), Article 3. https://doi.org/10.33998/jurnalmsi.2023.8.3.1490
Wali, M., Efitra, E., Sudipa, GI, Heryani, A., Hendriyani, C., (2023). Application & Implementation of Big Data in Various Sectors (Sustainable Development in the Era of Industry 4.0 and Society 5.0) . PT. Sonpedia Publishing Indonesia.
Wang, Z., Maalla, A., & Liang, M. (2021). Research on E-Commerce Personalized Recommendation System based on Big Data Technology. 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) , 2 ,909–913.
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