融合对话分析与情感识别，提升推荐系统个性化能力。
Conversational Recommendation System using NLP and Sentiment Analysis
- 结合NLP与情感分析，从对话中挖掘用户偏好。
- 融合内容与协同过滤，利用CNN、RNN等模型增强推荐效果。
- 适合营销场景中的智能客服与个性化推荐应用。
在数字化日益发展的今天，人们对个性化和上下文感知的推荐需求愈发强烈。传统推荐系统虽已取得显著进展，但往往难以充分利用对话数据的丰富信息。本文提出一种新型对话式推荐系统，将对话洞察融入推荐流程。该系统整合深度学习技术，采用Apriori算法进行关联规则挖掘，利用卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆网络（LSTM）建模用户行为。同时，通过隐马尔可夫模型（HMM）和动态时间规整（DTW）等语音识别技术实现高精度的语音转文本，确保在复杂环境下的稳定表现。方法融合内容基础与协同过滤策略，并引入自然语言处理技术，显著提升推荐的个性化与上下文感知能力，尤其适用于市场营销场景。
原文摘要 · Abstract (English)
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.
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