This paper presents a comprehensive overview of the evolving field of context-aware recommendation systems (CARS), which integrate machine learning algorithms with user behavior analysis to enhance the personalization of recommendations. The study delves into the significance of understanding the context in which recommendations are made, as this context can significantly influence user satisfaction and the effectiveness of the recommendations. We explore various machine learning techniques that have been employed to analyze user behavior and extract meaningful patterns that can inform recommendation systems. Additionally, we discuss the importance of real-time context awareness in adapting recommendations to changing user preferences and environmental conditions. The paper further investigates the challenges and limitations associated with implementing context-awareness, such as data privacy concerns and the computational complexity of real-time analysis. Finally, we outline potential future directions for research and development in this area, emphasizing the need for more robust algorithms and scalable solutions to address the complexities of context-aware recommendation systems.
Jackson, M. Context-Aware Recommendation Systems Using Machine Learning and User Behavior Analysis. Transactions on Applied Soft Computing, 2020, 2, 14. https://doi.org/10.69610/j.tasc.20201122
AMA Style
Jackson M. Context-Aware Recommendation Systems Using Machine Learning and User Behavior Analysis. Transactions on Applied Soft Computing; 2020, 2(2):14. https://doi.org/10.69610/j.tasc.20201122
Chicago/Turabian Style
Jackson, Michael 2020. "Context-Aware Recommendation Systems Using Machine Learning and User Behavior Analysis" Transactions on Applied Soft Computing 2, no.2:14. https://doi.org/10.69610/j.tasc.20201122
APA style
Jackson, M. (2020). Context-Aware Recommendation Systems Using Machine Learning and User Behavior Analysis. Transactions on Applied Soft Computing, 2(2), 14. https://doi.org/10.69610/j.tasc.20201122
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References
Burbules, N. C., & Callister, T. A. (2000). Watch IT: The Risks and Promises of Information Technologies for Education. Westview Press.
Breese, J. S., Jensen, D. D., & Saronowicz, J. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th conference on Uncertainty in artificial intelligence (pp. 43-52). Morgan Kaufmann.
Loh, H. T., Huang, M. S., & Lin, H. C. (2010). Understanding user behavior in social media: Temporal and session-based analysis. In Proceedings of the 2010 ACM Web Conference (pp. 275-284). ACM.
Zhang, M., Liu, Q., & Su, Z. (2001). Context-aware recommendation system for multimedia applications. In Proceedings of the 9th annual international conference on Mobile computing and networking (pp. 249-260). ACM.
Zhang, M., Liu, Q., & Su, Z. (2008). GEO-TIR: A novel approach for real-time recommendation adaptation. In Proceedings of the 15th annual international conference on World Wide Web (pp. 25-34). ACM.
Karypis, G., & Han, J. (2009). Collaborative filtering via alternating least squares. ACM Transactions on Knowledge Discovery from Data (TKDD), 3(1), 1-27.
Wang, J., Li, X., & Li, H. (2014). Deep learning based recommendation system for e-commerce. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 75-84). ACM.
Wang, X., Chen, Z., & Han, J. (2016). A reinforcement learning approach for real-time recommendation adaptation. In Proceedings of the 29th AAAI conference on artificial intelligence (pp. 4464-4470).
Feller, A., Karras, P., & Lai, C. (2010). Ethical issues in recommendation systems. In Proceedings of the 2010 4th international conference on Web intelligence (pp. 277-282). IEEE.
Loh, H. T., Huang, M. S., & Lin, H. C. (2009). Scalability of collaborative filtering algorithms. In Proceedings of the 2009 IEEE 29th international conference on distributed computing systems (pp. 229-238). IEEE.