User Feature Mining via Expanded Feature Vectors
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Solution Overview
Problem
Current user feature mining systems lack effectiveness in identifying core features of users and generating accurate virtual profiles for personalized services and marketing, as they often rely on limited sample sizes and inefficient feature vector expansion methods.
Innovation Solution
A system and method that utilize a trained binary model to generate expanded feature vectors from positive and negative samples, determine core features, and create virtual profiles by normalizing and comparing feature vectors, thereby enhancing user classification and personalized service strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional user feature mining methods are used, then the system is simpler to implement, but the identification accuracy of core user features deteriorates
Solution Approach 1:
The patent segments the user feature mining process into distinct modules: sample selection module that divides users into positive and negative samples, feature vector expansion module that generates multiple expanded feature vectors from each sample, and core feature determination module that identifies important features. This segmentation allows each module to be optimized independently, improving overall identification accuracy while managing system complexity through modular design.
Solution Approach 2:
The patent transforms the original feature vectors into expanded feature vectors by adding new dimensions. Specifically, it generates multiple expanded feature vectors from each sample through various transformations and combinations, thereby enriching the feature space and providing more comprehensive information for core feature identification, which directly improves measurement precision.
2Reliability
If larger sample sizes are used, then the reliability of user classification improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-selecting positive and negative samples based on specific criteria before the main classification process. This preliminary sample selection ensures that only high-quality, relevant samples are processed in subsequent steps, improving classification reliability while avoiding the need to process all available data, thus reducing processing time.
Solution Approach 2:
The patent changes parameters by transforming original feature vectors into multiple expanded feature vectors with different characteristics. This parameter transformation allows the system to extract more information from the same sample set, improving reliability without requiring proportionally larger sample sizes, thereby balancing reliability improvement with processing efficiency.
3Manufacturing precision
If more feature vectors are generated through expansion, then the precision of virtual profile generation improves, but the computational complexity increases
Solution Approach 1:
The patent extracts key information from original feature vectors and reconstructs multiple expanded feature vectors that highlight different aspects of user characteristics. By taking out essential features and recombining them in various ways, the system achieves higher precision in virtual profile generation while avoiding the need to process all possible feature combinations, thus managing computational complexity.
Solution Approach 2:
The expanded feature vectors serve multiple functions: they enhance virtual profile generation precision, support core feature identification, and provide robust input for user classification. This multi-functionality allows the system to achieve high precision across multiple tasks without requiring separate processing pipelines, thereby controlling overall computational complexity.
Data Source
AI summary
A method for user mining is provided. The method may include obtaining a plurality of first feature vectors of a plurality of positive samples and a plurality of second feature vectors of a plurality of negative samples, and generating a plurality of expanded first feature vectors based on the plurality of first feature vectors and second feature vectors and expanded second feature vectors. Each first feature vector may include first feature information that describes a plurality of features of a corresponding positive sample. Each second feature vector may include second feature information that describes a plurality of features of a corresponding negative sample. The method may further include determining one or more core features related to the plurality of positive samples among the plurality of features corresponding to the plurality of first feature vectors based on a trained binary model.