Paper Title :Conversational Recommendation System Using NLP and Sentiment Analysis
Author :Piyush Talegaonkar, Siddhant Hole, Shrinesh Kamble, PrashilGulechha, Deepali Salapurkar
Article Citation :Piyush Talegaonkar ,Siddhant Hole ,Shrinesh Kamble ,PrashilGulechha ,Deepali Salapurkar ,
(2024 ) " Conversational Recommendation System Using NLP and Sentiment Analysis " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 17-22,
Volume-12,Issue-6
Abstract : In today's digitally-driven world, the demand for personalized and context-aware recommendations has never
been greater. Traditional recommender systems have made significant strides in this direction, but they often lack the ability
to tap into the richness of conversational data. "Conversational Recommender System for Marketing Application using Deep
Learning,” represents a novel approach to recommendation systems by integrating conversational insights into the
recommendation process. The Conversational Recommender System integrates cutting-edge technologies such as deep
learning, leveraging machine learning algorithms like Apriori for Association Rule Mining [1], Convolutional Neural
Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LTSM) [10]. Furthermore,
sophisticated voice recognition technologies, including Hidden Markov Models (HMMs) and Dynamic Time Warping
(DTW) algorithms [13], play a crucial role in accurate speech-to-text conversion, ensuring robust performance in diverse
environments. The methodology incorporates a fusion of content-based and collaborative recommendation approaches,
enhancing them with NLP techniques. This innovative integration ensures a more personalized and context-aware
recommendation experience, particularly in marketing applications.
Keywords - Recommender System, Speech Recognition, Artificial Intelligence, Natural Language Processing, API,
Sentiment Analysis.
Type : Research paper
Published : Volume-12,Issue-6
Copyright: © Institute of Research and Journals
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Published on 2024-09-21 |
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