Integrating Machine Learning with Web Intelligence for Predictive Search and Recommendations

Authors

DOI:

https://doi.org/10.33050/italic.v3i1.654

Keywords:

Machine Learning, Web Intelligence, Search Systems, Recommendation Systems, Predictive Models

Abstract

This study examines the integration of Machine Learning (ML) with Web Intelligence (WI) as a transformative approach for enhancing web-based search and recommendation systems. The objective is to utilize the combined strengths of ML and WI to significantly increase the accuracy, precision, and relevance of predictions, providing personalized and context-aware results that adapt in real-time. Employing a hybrid model that leverages both the predictive capabilities of ML and the dynamic adaptability of WI, this research methodologically assesses the performance against traditional models through rigorous testing. Results indicate that the integrated system substantially outperforms conventional models, demonstrating enhanced performance metrics across accuracy, precision, and recall. Theoretically, this integration contributes to the advancement of WI frameworks, while practically, it offers significant improvements for real-world applications, especially in optimizing user interactions and satisfaction. However, the study also recognizes limitations related to the scalability of the data and models used. Future research should focus on refining model complexity and enhancing real-time data processing capabilities. Additionally, the integration of these technologies supports several Sustainable Development Goals (SDGs), particularly Goal 9 (Industry, Innovation, and Infrastructure) by promoting sustainable industrialization through advanced technologies, Goal 8 (Decent Work and Economic Growth) by fostering economic growth and employment in the tech sector, and Goal 12 (Responsible Consumption and Production) by enabling more informed consumer choices through better recommendations. These connections underline the role of innovative technologies in achieving sustainable development and enhancing global economic and social frameworks.

References

Z. Kedah, “Use of e-commerce in the world of business,” Startupreneur Business Digital (SABDA Journal), vol. 2, no. 1, pp. 51–60, 2023.

J. Westermayr, M. Gastegger, K. T. Sch¨utt, and R. J. Maurer, “Perspective on integrating machine learning into computational chemistry and materials science,” The Journal of Chemical Physics, vol. 154, no. 23, 2021.

I. Sembiring, D. Manongga, U. Rahardja, and Q. Aini, “Understanding data-driven analytic decision making on air quality monitoring an empirical study,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 3, pp. 418–431, 2024.

D. M. S. Zekrifa, M. Kulkarni, A. Bhagyalakshmi, N. Devireddy, S. Gupta, and S. Boopathi, “Integrating machine learning and ai for improved hydrological modeling and water resource management,” in Artificial Intelligence Applications in Water Treatment and Water Resource Management. IGI Global, 2023, pp. 46–70.

F. Ahmadi, M. Simchi, J. M. Perry, S. Frenette, H. Benali, J.-P. Soucy, G. Massarweh, and S. C. Shih, “Integrating machine learning and digital microfluidics for screening experimental conditions,” Lab on a Chip, vol. 23, no. 1, pp. 81–91, 2023.

V. Melinda, T. Williams, J. Anderson, J. G. Davies, and C. Davis, “Enhancing waste-to-energy conversion efficiency and sustainability through advanced artificial intelligence integration,” International Transactions on Education Technology (ITEE), vol. 2, no. 2, pp. 183–192, 2024.

K. K. R. Yanamala, “Integrating machine learning and human feedback for employee performance evaluation,” Journal of Advanced Computing Systems, vol. 2, no. 1, pp. 1–10, 2022.

M. Irawan and Z. A. Tyas, “Desain asset game android komodo isle berbasis 2 dimensi,” ADI Bisnis Digital Interdisiplin Jurnal, vol. 5, no. 1, pp. 58–66, 2024.

Y. A. Kadakia, A. Suryavanshi, A. Alnajdi, F. Abdullah, and P. D. Christofides, “Integrating machine learning detection and encrypted control for enhanced cybersecurity of nonlinear processes,” Computers & Chemical Engineering, vol. 180, p. 108498, 2024.

F. Wang and J. Aviles, “Enhancing operational efficiency: Integrating machine learning predictive capabilities in business intellgence for informed decision-making,” Frontiers in business, economics and management, vol. 9, no. 1, pp. 282–286, 2023.

J. Willard, X. Jia, S. Xu, M. Steinbach, and V. Kumar, “Integrating scientific knowledge with machine learning for engineering and environmental systems,” ACM Computing Surveys, vol. 55, no. 4, pp. 1–37, 2022.

M. R. Anwar and L. D. Sakti, “Integrating artificial intelligence and environmental science for sustainable urban planning,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 5, no. 2, pp. 179–191, 2024.

J. K. Li and K.-L. Ma, “P6: A declarative language for integrating machine learning in visual analytics,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 380–389, 2020.

U. Rusilowati, H. R. Ngemba, R. W. Anugrah, A. Fitriani, and E. D. Astuti, “Leveraging ai for superior efficiency in energy use and development of renewable resources such as solar energy, wind, and bioenergy,” International Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 114–120, 2024.

R. A. Schwalbert, T. Amado, G. Corassa, L. P. Pott, P. V. Prasad, and I. A. Ciampitti, “Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern brazil,” Agricultural and Forest Meteorology, vol. 284, p. 107886, 2020.

C. Krittanawong, A. J. Rogers, K. W. Johnson, Z. Wang, M. P. Turakhia, J. L. Halperin, and S. M. Narayan, “Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management,” Nature Reviews Cardiology, vol. 18, no. 2, pp. 75–91, 2021.[17] S. Purnama and C. S. Bangun, “Strategic management insights into housewives consumptive shopping

behavior in the post covid-19 landscape,” APTISI Transactions on Management, vol. 8, no. 1, pp. 71–79, 2024.

N. Nuryani, A. B. Mutiara, I. M. Wiryana, D. Purnamasari, and S. N. W. Putra, “Artificial intelligence model for detecting tax evasion involving complex network schemes,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 3, pp. 339–356, 2024.

H. Chun, E. Lee, K. Nam, J.-H. Jang, W. Kyoung, S. H. Noh, and B. Han, “First-principle-data-integrated machine-learning approach for high-throughput searching of ternary electrocatalyst toward oxygen reduction reaction,” Chem Catalysis, vol. 1, no. 4, pp. 855–869, 2021.

Z. Li, W. Sun, D. Zhan, Y. Kang, L. Chen, A. Bozzon, and R. Hai, “Amalur: Data integration meets machine learning,” IEEE Transactions on Knowledge and Data Engineering, 2024.

I. Erliyani, K. Yuliana, H. Kusumah, and N. Aziz, “Metode pembelajaran dalam memberikan pendidikan agama islam pada usia dini industri 4.0,” Alfabet Jurnal Wawasan Agama Risalah Islamiah, Teknologi dan Sosial, vol. 1, no. 1, pp. 96–105, 2021.

S. Wijono, U. Rahardja, H. D. Purnomo, N. Lutfiani, and N. A. Yusuf, “Leveraging machine learning models to enhance startup collaboration and drive technopreneurship,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 3, pp. 432–442, 2024.

F. S. Putri, H. R. Ngemba, S. Hendra, and W. Wirdayanti, “Sistem layanan ujian psikotes sim menggunakan computer based test berbasis website: Sim psychological test service system using computer based test based on website,” Technomedia Journal, vol. 9, no. 1, pp. 92–104, 2024.

G.-W. Ji, K. Wang, Y.-X. Xia, J.-S. Wang, X.-H. Wang, and X.-C. Li, “Integrating machine learning and tumor immune signature to predict oncologic outcomes in resected biliary tract cancer,” Annals of surgical oncology, vol. 28, pp. 4018–4029, 2021.

A. G. Prawiyogi, A. S. Anwar et al., “Perkembangan internet of things (iot) pada sektor energi: Sistematik

literatur review,” Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi, vol. 1, no. 2, pp. 187–197, 2023.

E. Dodangeh, B. Choubin, A. N. Eigdir, N. Nabipour, M. Panahi, S. Shamshirband, and A. Mosavi, “Integrated machine learning methods with resampling algorithms for flood susceptibility prediction,” Science of the Total Environment, vol. 705, p. 135983, 2020.

A. Martinez, A. Fitzroy, and A. Hogwart, “Network communication security: Challenges and solutions in the digital era,” International Journal of Cyber and IT Service Management, vol. 4, no. 1, pp. 46–51, 2024.

K. Myers and C. R. Hinman, “The impact of cryptocurrency on the indonesian community’s economy,” Blockchain Frontier Technology, vol. 3, no. 1, pp. 74–79, 2023.

A. Leffia, S. A. Anjani, M. Hardini, S. V. Sihotang, and Q. Aini, “Corporate strategies to improve platform economic performance: The role of technology, ethics, and investment management,” CORISINTA, vol. 1, no. 1, pp. 16–25, 2024.

J. Kang, A. K. Chowdhry, S. L. Pugh, and J. H. Park, “Integrating artificial intelligence and machine learning into cancer clinical trials,” in Seminars in Radiation Oncology, vol. 33, no. 4. Elsevier, 2023, pp. 386–394.

H. Y. N. Heri, “The effect of fragmentation as a moderation on the relationship between supply chain management and project performance,” ADI Journal on Recent Innovation, vol. 6, no. 1, pp. 90–101, 2024.

S. Bi and Y. Lian, “Advanced portfolio management in finance using deep learning and artificial intelligence techniques: Enhancing investment strategies through machine learning models,” Journal of Artificial Intelligence Research, vol. 4, no. 1, pp. 233–298, 2024.

Z. I. A. Ajwa et al., “Harnessing ai technologies: Innovations in literacy libraries for diverse learners,” International Journal of Cyber and IT Service Management, vol. 4, no. 1, pp. 19–25, 2024.

M. L. Gordon, M. S. Lam, J. S. Park, K. Patel, J. Hancock, T. Hashimoto, and M. S. Bernstein, “Jury learning: Integrating dissenting voices into machine learning models,” in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, 2022, pp. 1–19.

J. Willard, X. Jia, S. Xu, M. Steinbach, and V. Kumar, “Integrating physics-based modeling with machine learning: A survey,” arXiv preprint arXiv:2003.04919, vol. 1, no. 1, pp. 1–34, 2020.

M. Karimi-Mamaghan, M. Mohammadi, P. Meyer, A. M. Karimi-Mamaghan, and E.-G. Talbi, “Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art,” European Journal of Operational Research, vol. 296, no. 2, pp. 393–422, 2022.

N. Cholisoh, R. W. Anugrah, M. F. Fazri, S. M. Wahid, and R. D. Pramudya, “Optimizing engagement dynamics in e-learning environments with insights and strategic approaches,” in 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT). IEEE, 2024, pp. 1–6.

U. Serencam, ¨O. Ekmekcio˘glu, E. E. Bas¸akın, and M. ¨Ozger, “Determining the water level fluctuations of lake van through the integrated machine learning methods,” International Journal of Global Warming, vol. 27, no. 2, pp. 123–142, 2022.

N. F. A. M. Fadzil, H. M. Fadzir, H. Mansor, and U. Rahardja, “Driver behaviour classification: A research using obd-ii data and machine learning,” Journal of Advanced Research in Applied Sciences and Engineering Technology, pp. 51–61, 2024.

S. Rivas, H. Cox, B. Mescher, C. Villamin, T. Bates, F. Martinez, M. Correa Medina, and K. Klein, “Predictive modeling to create a proactive approach to patient blood management in the oncology population.” 2021.

A. A. Nichol, J. N. Batten, M. C. Halley, J. K. Axelrod, P. L. Sankar, and M. K. Cho, “A typology of existing machine learning–based predictive analytic tools focused on reducing costs and improving quality in health care: Systematic search and content analysis,” Journal of medical Internet research, vol. 23, no. 6, p. e26391, 2021.

G. Tse, Q. Lee, O. H. I. Chou, C. T. Chung, S. Lee, J. S. K. Chan, G. Li, N. Kaur, L. Roever, H. Liu et al., “Healthcare big data in hong kong: Development and implementation of artificial intelligence-enhanced predictive models for risk stratification,” Current Problems in Cardiology, vol. 49, no. 1, p. 102168, 2024.

M. Kolukuluri, V. K. Devi, S. S. Tejaswini, and K. Anusha, “Business intelligence using data mining techniques and predictive analytics,” Journal of Pharmaceutical Negative Results, pp. 6923–6932, 2023.

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Published

2024-11-11