A Collaborative Filtering Approach Using Machine Learning and Business Intelligence: A Critical Review
Keywords:
A Collaborative Filtering, Machine Learning, Business IntelligenceAbstract
In today's digital context, internet buying has become a common way of consumer behaviour, necessitating the creation of highly personalised recommendation systems. This study provides a critical analysis of a collaborative filtering technique that uses machine learning and business intelligence (BI) to improve e-commerce recommendation systems. By reviewing the existing literature, we uncover considerable gaps in current research, particularly in the successful use of large data and advanced artificial intelligence techniques. Our findings show that combining deep learning with reinforcement learning can significantly increase suggestion reliability and responsiveness to user preferences. Furthermore, we present a comprehensive framework for analysing large datasets using collaborative filtering and BI tools, resulting in actionable insights into customer behaviour, market trends, and product performance. This integration not only improves the suggestion process, but it also creates a more interesting and pleasant buying experience for users. Finally, this study emphasises the importance of continued research in personalised recommendation systems in order to fully leverage future e-commerce technology. The investigation demonstrates that traditional recommendation methods frequently fail to give meaningful ideas, with user satisfaction percentages as low as 60% in some tests. In contrast, our suggested architecture, which integrates collaborative filtering and BI technologies, shows a considerable increase in suggestion accuracy. Specifically, we discovered that combining deep learning techniques with reinforcement learning algorithms enhanced recommendation reliability by 35% while improving user engagement measures by 25%. Furthermore, the incorporation of BI tools improved data visualisation and predictive analytics, allowing e-commerce companies to better understand customer behaviour and market trends. This study emphasises the importance of continued research and innovation in personalised recommendation systems, advocating for a comprehensive approach that leverages the potential of emerging technologies to satisfy consumers' growing expectations in the competitive e-commerce landscapeReferences
M. Pham, "Exploring privacy concerns and consumer behaviors towards personalization tools," 2024.
M. A. Raji, H. B. Olodo, T. T. Oke, W. A. Addy, O. C. Ofodile, and A. T. Oyewole, "E-commerce and consumer behavior: A review of AI-powered personalization and market trends," GSC Advanced Research and Reviews, vol. 18, no. 3, pp. 066–077, 2024.
D.-N. Nguyen, et al., "A personalized product recommendation model in e-commerce based on retrieval strategy," Journal of Open Innovation: Technology, Market, and Complexity, vol. 10, no. 2, 2024, Art. no. 100303.
D. Rukiya, T. Africa, S. Nayak, and S. Nooreain, "Recommendation in E-Commerce using Collaborative Filtering," International Research Journal of Engineering and Technology, vol. 6, no. 5, 2019.
V. Sakalauskas and D. Kriksciuniene, "Personalized advertising in e-commerce: Using clickstream data to target high-value customers," Algorithms, vol. 17, no. 1, p. 27, 2024.
K. Züllig, "Context-aware marketing attribution based on survival analysis," 2023.
J. H. Blümel, Providing Personalised Experience in Text-based Customer Service Conversations, Doctoral dissertation, 2024.
S. Vijayakumar and G. Deepak, "DF e-commerce: A deep learning integrated ontology-driven model for e-commerce product recommendation for improved machine intelligence," in International Conference on Digital Technologies and Applications, Cham: Springer International Publishing, Jan. 2022, pp. 209–218.
N. M. Ibrahim and M. F. Hassan, "Mathematical modeling using coordination mechanisms for multi-agent systems in service-oriented architecture," Journal of Information Systems and Digital Technologies, vol. 1, no. 2, pp. 48–63, 2019.
L. Wei and Z. Xia, "Big data-driven personalization in e-commerce: Algorithms, privacy concerns, and consumer behavior implications," International Journal of Applied Machine Learning and Computational Intelligence, vol. 12, no. 4, 2022.
N. M. Ibrahim, N. B. Idris, M. K. Hassan, C. Breathnach, and A. A. Hussin, "Big data processing using Hadoop HDFS and Map-Reduce for public open data (POD)," 2021.
V. Chandna and M. S. Salimath, "Co-creation of value in platform-dependent entrepreneurial ventures," Electronic Commerce Research, pp. 1–30, 2022.
R. Chen, Q. Hua, Y. S. Chang, B. Wang, L. Zhang, and X. Kong, "A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks," IEEE Access, vol. 6, pp. 64301–64320, 2018.
L. J. Chew, S. C. Haw, and S. Subramaniam, "Recommender system for retail domain: An insight on techniques and evaluations," in Proceedings of the 12th International Conference on Computer Modeling and Simulation, June 2020, pp. 9–13.
F. Demira?, "Web personalization: Consumer perspective," in Globalized Consumer Insights in the Digital Era, IGI Global, 2024, pp. 169–185.
P. Dräxler, "E-commerce platform designed for continuous optimization and personalization," 2023.
N. N. Duy, V. H. Nguyen, T. Trinh, T. Ho, and H. S. Le, "A personalized product recommendation model in e-commerce based on retrieval strategy," Journal of Open Innovation: Technology, Market, and Complexity, vol. 10, no. 2, 2024, Art. no. 100303.
Z. H. Hu, X. Li, C. Wei, and H. L. Zhou, "Examining collaborative filtering algorithms for clothing recommendation in e-commerce," Textile Research Journal, vol. 89, no. 14, pp. 2821–2835, 2019.
A. Kumar, A. Mudgal, A. L. Yadav, and A. Sharma, "Cloudsuggest: Enhancing e-commerce with personalized recommendations," in 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT), Feb. 2024, vol. 5, pp. 763–766.
S. C. Lee, S. W. Kim, S. Park, and D. K. Chae, "A tripartite-graph-based recommendation framework for price-comparison services," Computer Science and Information Systems.
L. Liu, "E-commerce personalized recommendation based on machine learning technology," Mobile Information Systems, 2022.
S. M. Phal and S. Srivastava, "An analysis of machine learning methods for ranking in recommendation systems," 2020.
M. S. Ahmad, "Recent research in recommender systems," Journal of Global Research in Computer Science, vol. 9, no. 6, pp. 12–14, 2018.
R. J. K. Almahmood and A. Tekerek, "Issues and solutions in deep learning-enabled recommendation systems within the e-commerce field," Applied Sciences, vol. 12, no. 21, p. 11256, 2022.
A. Asad, M. M. Fouda, Z. M. Fadlullah, M. I. Ibrahem, and N. Nasser, "Moreau envelopes-based personalized asynchronous federated learning: Improving practicality in network edge intelligence," in GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Dec. 2023, pp. 2033–2038.
A. M. As’ad, Moreau envelopes-based personalized asynchronous federated learning: Improving practicality in distributed machine learning, Doctoral dissertation, 2023.
A. Bielozorov, M. Bezbradica, and M. Helfert, "The role of user emotions for content personalization in e-commerce: Literature review," in HCI in Business, Government and Organizations: eCommerce and Consumer Behavior, Springer International Publishing, 2019, pp. 177–193.
M. A. Raji, H. B. Olodo, T. T. Oke, W. A. Addy, O. C. Ofodile, and A. T. Oyewole, "E-commerce and consumer behavior: A review of AI-powered personalization and market trends," GSC Advanced Research and Reviews, vol. 18, no. 3, pp. 066–077, 2024.
V. Sakalauskas and D. Kriksciuniene, "Personalized advertising in e-commerce: Using clickstream data to target high-value customers," Algorithms, vol. 17, no. 1, 2024.
J. H. Blümel, Providing Personalised Experience in Text-based Customer Service Conversations, Doctoral dissertation, 2024.
