Computational Intelligence and Machine Learning
AI-Driven NLP Solutions for Intelligent Recommendation Systems Deep Learning Meets NLP and Recommender Systems-Practical and Research-Oriented Insights
Authors: Suharsh Anand and Harish Sharma
Publishing Date: 09-05-2025
ISBN: 978-81-975670-5-6
Abstract
The value of intelligent recommendation systems increases because users want personalized digital encounters to boost their satisfaction levels by increasing participation. Recommended frameworks that achieve accuracy result from combining deep learning patterns with Natural Language Processing base functionalities used in AI-based systems. The integration of advanced technologies into Enterprise Resource Planning systems has transformed how businesses interact with data. A simulation platform processed data from tested software by combining sentiment data from text analytic evaluation with performance time data and session activity patterns that contained item linkage results. The system becomes capable of delivering more exact context-aware recommendations because it operates with precise multipatterned information at various levels which exactly mirrors user experiences and content management interactions. Random Forest algorithm provides accurate predictions together with transparent interpretability features. The experimental results achieved by using simulated conditions confirm that the developed framework meets readiness criteria for practical implementation.
Keywords
AI-driven recommendations, Natural Language Processing, Deep Learning, User behavior modeling, Sentiment analysis, SAP, ERP, Random Forest, Recommendation systems, and Explainable AI.
Cite as
Suharsh Anand and Harish Sharma, "AI-Driven NLP Solutions for Intelligent Recommendation Systems Deep Learning Meets NLP and Recommender Systems-Practical and Research-Oriented Insights", In: Sandeep Kumar and Kavita Sharma (eds), Computational Intelligence and Machine Learning, SCRS, India, 2025, pp. 149-172. https://doi.org/10.56155/978-81-975670-5-6-12