Change Management in Recommender Systems: Algorithms and Evaluation Metrics for Enhancing User Satisfaction and Decision-Making
Keywords:
Recommender Systems, AlgorithmsAbstract
Recommender systems play a crucial role in modern information retrieval and e-commerce platforms by providing personalized recommendations that facilitate change management in user engagement and business strategies. This paper offers a comprehensive review of recommender system algorithms and evaluation metrics, emphasizing their significance in predictive change management within information systems projects. We first discuss the importance of recommender systems and their impact on user satisfaction and business performance, particularly in dynamic environments. Then, we review the most widely used recommendation algorithms, including collaborative filtering, content-based filtering, and hybrid approaches. Next, we delve into evaluation metrics used to assess the performance of recommender systems, such as precision, recall, and mean absolute error, while also addressing advanced evaluation techniques, including offline and online evaluation methods. Finally, we highlight challenges and future research directions in the field of recommender systems, focusing on how these systems can adapt to changing user preferences and business needs.
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References
Vemoori, Vamsi. "Envisioning a Seamless Multi-Modal Transportation Network: A Framework for Connected Intelligence, Real-Time Data Exchange, and Adaptive Cybersecurity in Autonomous Vehicle Ecosystems." Australian Journal of Machine Learning Research & Applications 4.1 (2024): 98-131.
Prabhod, Kummaragunta Joel. "AI-Driven Insights from Large Language Models: Implementing Retrieval-Augmented Generation for Enhanced Data Analytics and Decision Support in Business Intelligence Systems." Journal of Artificial Intelligence Research 3.2 (2023): 1-58.
Sadhu, Ashok Kumar Reddy, et al. "Enhancing Customer Service Automation and User Satisfaction: An Exploration of AI-powered Chatbot Implementation within Customer Relationship Management Systems." Journal of Computational Intelligence and Robotics 4.1 (2024): 103-123.
Tatineni, Sumanth. "Applying DevOps Practices for Quality and Reliability Improvement in Cloud-Based Systems." Technix international journal for engineering research (TIJER)10.11 (2023): 374-380.
Perumalsamy, Jegatheeswari, Bhavani Krothapalli, and Chandrashekar Althati. "Machine Learning Algorithms for Customer Segmentation and Personalized Marketing in Life Insurance: A Comprehensive Analysis." Journal of Artificial Intelligence Research 2.2 (2022): 83-123.
Venkatasubbu, Selvakumar, Subhan Baba Mohammed, and Monish Katari. "AI-Driven Storage Optimization in Embedded Systems: Techniques, Models, and Real-World Applications." Journal of Science & Technology 4.2 (2023): 25-64.
Devan, Munivel, Bhavani Krothapalli, and Lavanya Shanmugam. "Advanced Machine Learning Algorithms for Real-Time Fraud Detection in Investment Banking: A Comprehensive Framework." Cybersecurity and Network Defense Research 3.1 (2023): 57-94.
Althati, Chandrashekar, Bhavani Krothapalli, and Bhargav Kumar Konidena. "Machine Learning Solutions for Data Migration to Cloud: Addressing Complexity, Security, and Performance." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 38-79.
Selvaraj, Amsa, Bhavani Krothapalli, and Lavanya Shanmugam. "AI and Machine Learning Techniques for Automated Test Data Generation in FinTech: Enhancing Accuracy and Efficiency." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 329-363.
Konidena, Bhargav Kumar, Jesu Narkarunai Arasu Malaiyappan, and Anish Tadimarri. "Ethical Considerations in the Development and Deployment of AI Systems." European Journal of Technology 8.2 (2024): 41-53.
Devan, Munivel, et al. "AI-driven Solutions for Cloud Compliance Challenges." AIJMR-Advanced International Journal of Multidisciplinary Research 2.2 (2024).
Pelluru, Karthik. "Enhancing Network Security: Machine Learning Approaches for Intrusion Detection." MZ Computing Journal 4.2 (2023).
Katari, Monish, Gowrisankar Krishnamoorthy, and Jawaharbabu Jeyaraman. "Novel Materials and Processes for Miniaturization in Semiconductor Packaging." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 2.1 (2024): 251-271.
Tatineni, Sumanth, and Naga Vikas Chakilam. "Integrating Artificial Intelligence with DevOps for Intelligent Infrastructure Management: Optimizing Resource Allocation and Performance in Cloud-Native Applications." Journal of Bioinformatics and Artificial Intelligence 4.1 (2024): 109-142.
Katari, Monish, Lavanya Shanmugam, and Jesu Narkarunai Arasu Malaiyappan. "Integration of AI and Machine Learning in Semiconductor Manufacturing for Defect Detection and Yield Improvement." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 3.1 (2024): 418-431.
Prakash, Sanjeev, et al. "Achieving regulatory compliance in cloud computing through ML." AIJMR-Advanced International Journal of Multidisciplinary Research 2.2 (2024).
Makka, A. K. A. “Administering SAP S/4 HANA in Advanced Cloud Services: Ensuring High Performance and Data Security”. Cybersecurity and Network Defense Research, vol. 2, no. 1, May 2022, pp. 23-56, https://thesciencebrigade.com/cndr/article/view/285.
Peddisetty, Namratha, and Amith Kumar Reddy. "Leveraging Artificial Intelligence for Predictive Change Management in Information Systems Projects." Distributed Learning and Broad Applications in Scientific Research 10 (2024): 88-94.
Venkataramanan, Srinivasan, et al. "Leveraging Artificial Intelligence for Enhanced Sales Forecasting Accuracy: A Review of AI-Driven Techniques and Practical Applications in Customer Relationship Management Systems." Australian Journal of Machine Learning Research & Applications 4.1 (2024): 267-287.
Althati, Chandrashekar, Jesu Narkarunai Arasu Malaiyappan, and Lavanya Shanmugam. "AI-Driven Analytics: Transforming Data Platforms for Real-Time Decision Making." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 3.1 (2024): 392-402.
Venkatasubbu, Selvakumar, and Gowrisankar Krishnamoorthy. "Ethical Considerations in AI Addressing Bias and Fairness in Machine Learning Models." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 1.1 (2022): 130-138.
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