Information Recommendation System Using NLP for Personalized Securities
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Solution Overview
Problem
Current stock recommendation methods are generic and fail to meet the individual needs of users, lacking personalized strategies that account for user preferences and financial expertise.
Innovation Solution
A method and system for information recommendation that involves obtaining user-selected or retrieved information, analyzing it, determining a retrieval path, and recommending related securities information based on machine learning and natural language processing, with features like keyword extraction, priority determination, and evaluation to provide tailored investment strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generic stock recommendation methods are used, then the system complexity is low, but the recommendation accuracy and user satisfaction deteriorate
Solution Approach 1:
The patent segments the recommendation system into multiple independent modules: user profile analysis module, information retrieval module, natural language processing module, and recommendation generation module. Each module handles specific tasks independently, improving recommendation accuracy through specialized processing while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary natural language processing layer that mediates between user queries and the recommendation engine. This intermediary translates user intent into structured parameters, enabling accurate recommendations without requiring direct complex interactions between all system components.
2Adaptability or versatility
If personalized recommendation strategies are implemented, then user satisfaction improves, but the information processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing user profile information, preferences, and historical behavior data in a structured format before actual recommendation requests. This advance preparation enables rapid personalized recommendation generation without extensive real-time processing.
Solution Approach 2:
The patent applies local quality by selectively processing only the most relevant user attributes and information sources for each specific recommendation request, rather than analyzing all available data uniformly. This targeted approach maintains personalization quality while reducing overall processing time.
3Reliability
If comprehensive information analysis is performed, then recommendation quality improves, but the computational resources required increase
Solution Approach 1:
The patent implements partial action by analyzing only the most critical information dimensions and user attributes necessary for each recommendation scenario, rather than comprehensively processing all available data. This selective analysis maintains adequate recommendation quality while significantly reducing computational resource consumption.
Data Source
AI summary
The disclosure relates to information recommendation systems and methods. The information recommendation methods may include: obtaining information selected by a user or information retrieved by the user; analyzing the selected information or the retrieval information; determining a retrieval path based on a result of analyzing the selected information or the retrieval information; retrieving other information related to the selected information based on the retrieval path; and recommend the other information to the user. The information recommendation systems may include a computer-readable storage medium; codes stored in the computer-readable storage medium; and a processor; when executing the codes, the processor may perform the above-mentioned information recommendation methods.