Behavior Eigenvalue Application Recommendation
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Solution Overview
Problem
Existing application recommending methods on web social platforms suffer from low recommendation accuracy as they primarily rely on users' interests, failing to accurately suggest applications based on behavior data.
Innovation Solution
The method involves acquiring a behavior eigenvalue from user behavior data, determining similar users from a user relationship chain with a preset threshold, and recommending applications based on the behavior data of these similar users, utilizing a distributed computing approach to enhance real-time performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If application recommendation is based on user interest categories and category attributes, then the recommendation system can be implemented, but the recommendation accuracy is low
Solution Approach 1:
The patent transforms user behavior data into behavior eigenvalues through parameter transformation. Instead of directly using raw behavior data or simple interest categories, the system extracts behavioral features and represents them as eigenvalues that capture essential user preferences. This parameter change enables more accurate matching between user behavior patterns and application characteristics, resolving the contradiction between implementation feasibility and recommendation accuracy.
Solution Approach 2:
The patent introduces behavior eigenvalues as an intermediary between raw user behavior data and application recommendation. These eigenvalues serve as a mediating representation that bridges the gap between complex behavior data and the recommendation algorithm, enabling accurate information extraction while maintaining system implementability. The eigenvalues act as a compressed, meaningful representation that preserves critical behavioral patterns.
2Measurement precision
If application recommendation is based on users with similar interests using k-nearest neighbor or SVM, then the system can leverage social relationships, but the recommendation accuracy remains low due to noise in user relationships
Solution Approach 1:
The patent extracts behavior eigenvalues from comprehensive user behavior data, separating the essential behavioral patterns from noise and irrelevant information. By extracting only the most representative behavioral features into eigenvalues, the system reduces the impact of noisy user relationship data while maintaining the ability to identify truly similar users. This extraction process filters out irrelevant social connections and focuses on behaviorally similar users.
Solution Approach 2:
The patent segments the user similarity determination process into two independent components: behavior eigenvalue calculation and user matching. Instead of relying on a single complex similarity metric that combines social relationships and behavior, the system separately computes behavior eigenvalues and uses them as the primary basis for identifying similar users. This segmentation allows the system to leverage social relationships without being dominated by their noise, improving both accuracy and computational efficiency.
Data Source
AI summary
An application recommending method includes acquiring a first behavior eigenvalue according to behavior data of a first user. It is determined that at least one second user from a user relationship chain of the first user according to the first behavior eigenvalue. The second user is on the user relationship chain of the first user. A degree of similarity between a second behavior eigenvalue and the first behavior eigenvalue is greater than a preset threshold, and the second behavior eigenvalue is a behavior eigenvalue of the second user. A to-be-recommended application for the first user on the basis of behavior data of the at least one second user is determined.