Conversational Recommendation System using NLP and Sentiment Analysis
This work addresses the need for better personalized recommendations in digital contexts, but it appears incremental as it combines existing methods like deep learning and NLP without introducing a fundamentally new paradigm.
The paper tackles the problem of traditional recommender systems lacking conversational insights by integrating NLP and sentiment analysis with deep learning and voice recognition technologies, resulting in a more personalized and context-aware recommendation experience for marketing applications.
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. This paper 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, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LTSM). Furthermore, sophisticated voice recognition technologies, including Hidden Markov Models (HMMs) and Dynamic Time Warping (DTW) algorithms, 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.