Conversational Recommendation System using NLP and Sentiment Analysis

TL;DR

This paper presents a novel conversational recommender system that combines NLP, sentiment analysis, deep learning, and voice recognition technologies to provide personalized, context-aware recommendations in marketing.

Contribution

It introduces an integrated approach combining conversational data, deep learning, and voice recognition to enhance recommendation accuracy and personalization.

Findings

Improved recommendation relevance through conversational insights

Effective speech-to-text conversion in diverse environments

Enhanced personalization with NLP and sentiment analysis

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. 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.