Feature Information Extraction Using User and Package Attributes
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Solution Overview
Problem
Existing methods for extracting feature information from users or groups using virtual item packages in network applications only consider attribute information of the packages, lacking the analysis of user attributes, which affects the accuracy, efficiency, and security of the extraction process.
Innovation Solution
A method and apparatus that utilize machine learning models, specifically a recurrent neural network and an attention mechanism, to extract feature vectors from virtual item packages and importance fractions, considering both package and user attributes, thereby enhancing the extraction of feature information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only package attribute information is used for feature extraction, then the extraction process is simple, but the accuracy and reliability of user behavior analysis deteriorates
Solution Approach 1:
The patent segments the feature extraction process into two distinct stages: first extracting package attribute information, then extracting user attribute information. This segmentation allows the system to incorporate multiple information sources for improved accuracy while maintaining a structured and manageable extraction process, resolving the contradiction between comprehensive analysis and process simplicity.
Solution Approach 2:
The patent merges package attribute information and user attribute information through a fusion mechanism that combines both data sources. This merging enables the system to achieve higher measurement precision by considering both what packages users buy and user-specific characteristics, while the structured combination approach keeps the overall system complexity可控.
2Reliability
If traditional counting methods are used to extract feature information, then the processing speed is fast, but the reliability of user behavior classification deteriorates
Solution Approach 1:
The patent performs preliminary extraction of user attributes (such as user ID, level, and other characteristic features) before the main classification process. This preliminary action prepares the data in advance, enabling more reliable user behavior classification while maintaining processing efficiency through pre-organized information structures.
Solution Approach 2:
The patent replaces traditional mechanical counting methods with an information fusion approach that incorporates machine learning techniques. This substitution improves reliability by considering multiple factors beyond simple package counts, including user-specific attributes and behavioral patterns, while the automated nature of the fusion process maintains high processing efficiency.
3Measurement precision
If comprehensive user and package attributes are analyzed, then the classification accuracy improves, but the computational resources and time required increases
Solution Approach 1:
The patent segments the comprehensive attribute analysis into distinct modules: package attribute extraction, user attribute extraction, and information fusion. This segmentation allows parallel processing of different attribute types, reducing overall extraction time while maintaining comprehensive analysis for high classification accuracy.
Solution Approach 2:
The patent implements a flexible information fusion mechanism that can adaptively select which user and package attributes to analyze based on the specific classification task. This partial action approach ensures comprehensive analysis when needed for high accuracy, while allowing selective reduction of analyzed attributes when processing time is constrained, thus balancing accuracy and time requirements.
Data Source
AI summary
This application relates to a feature information extraction method and apparatus, a server cluster, and a storage medium. In various implementations, package attribute vectors respectively corresponding to at least two virtual item packages of a target object may be obtained. Feature extraction may be performed on these package attribute vectors to obtain feature vectors. Using the feature vector feature information may be obtained for the virtual item packages. In this way, differences between users of different attributes when the users are using virtual item packages may be considered, thereby improving the accuracy, efficiency and security of feature information extraction.