Chatbot News Recommendation via Sentiment Analysis
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Solution Overview
Problem
Current AI chatbots lack efficient methods for providing personalized and interactive news recommendations to users, failing to effectively utilize user preferences and sentiment analysis in automated chatting scenarios.
Innovation Solution
A chatbot system that obtains user messages, determines news recommendations based on user preferences and sentiment labels, and provides personalized news through title, summarization, content, and sentiment analysis, using a knowledge-based QA set and topic graph to offer relevant news and answers to user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If AI chatbot provides automated chatting service, then user interaction capability is improved, but personalized news recommendation capability deteriorates
Solution Approach 1:
The system segments the chatbot functionality into distinct modules: one handling automated chatting service and another dedicated to personalized news recommendation. This segmentation allows each module to specialize in its function while working together through shared user preference data and sentiment analysis results, resolving the contradiction between interaction capability and recommendation capability.
Solution Approach 2:
The chatbot system is designed with multi-functionality, simultaneously providing both automated chatting service and personalized news recommendation. By integrating user preference lists and sentiment analysis into the core architecture, the system achieves universal capability to handle both conversational interactions and personalized content delivery without compromising either function.
2Measurement precision
If sentiment analysis is applied to news recommendation, then personalization accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The system performs preliminary action by pre-establishing user preference lists and pre-computing sentiment analysis models before actual news recommendation occurs. User preferences are collected and stored in advance, and sentiment analysis frameworks are prepared beforehand, allowing the recommendation engine to quickly match news with user preferences without complex real-time computations, thus improving accuracy while managing complexity.
3Productivity
If news recommendation is provided during automated chatting, then user engagement is improved, but information retrieval efficiency deteriorates
Solution Approach 1:
The system implements feedback mechanisms where user interactions during automated chatting continuously refine the personalization model. User preferences are updated based on chatting behavior, and sentiment analysis results are fed back to improve future recommendations. This feedback loop enhances user engagement over time while making information retrieval more efficient as the system learns user patterns and can provide more targeted recommendations with fewer queries.
Data Source
AI summary
The present disclosure provides method and apparatus for providing news recommendation in automated chatting. A first message may be obtained in a chat flow. Recommended news may be determined based at least on the first message, a user preference list including sentiment labels, and a news dataset including sentiment labels. At least one of title, summarization and content of the recommended news may be provided based on the first message.