• Atharv Mohan Tad
• Akshay Deepak Gaikwad
• Prasiddhi Mahendra Ghase
• Megha Mohan

This paper presents the development of an advanced AI-driven recommendation system designed to improve mobile shopping experiences through sentiment analysis and machine learning techniques. The proposed system, SmartMobileXperience, integrates content-based filtering using Euclidean distance and deep learning models, particularly Long Short-Term Memory (LSTM) networks, to analyze user sentiments and deliver personalized recommendations. By capturing and processing consumer reviews, the system dynamically adjusts suggestions to align with user preferences, ensuring a more intuitive and engaging shopping experience. The methodology encompasses data collection from major e-commerce platforms, preprocessing of text based data using Natural Language Processing (NLP) techniques, and training an LSTM model to classify sentiments effectively. The recommendation engine further refines selections by prioritizing positively reviewed products, improving both accuracy and relevance. Experimental results demonstrate a significant enhancement in recommendation precision, user satisfaction, and engagement compared to traditional systems. This study highlights the potential of integrating sentiment-aware AI into recommendation frameworks, paving the way for more adaptive and user-centric e-commerce solutions.

Recommendation System, Sentiment Analysis, Machine Learning, LSTM, Natural Language Processing (NLP), Content-Based Filtering, E-Commerce Personalization, Deep Learning, User Experience, Adaptive AI.

"AI-enhanced User Interface : Transforming Mobile Shopping wih Sentiment Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.d476-d490, March-2025, Available :http://www.jetir.org/papers/JETIR2503352.pdf

"AI-enhanced User Interface : Transforming Mobile Shopping wih Sentiment Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppd476-d490, March-2025, Available at : http://www.jetir.org/papers/JETIR2503352.pdf