Software Recommendation via User Cluster Analysis
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Solution Overview
Problem
Existing software management systems fail to accurately recommend target software to users due to low relevance between recommended software and user-specific needs, leading to ineffective software recommendations.
Innovation Solution
A method and system that perform cluster analysis on user software usage data to determine user clusters, sort software lists based on usage conditions, and recommend top-ranked software from relevant clusters to individual users, enhancing the relevance and accuracy of recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional software recommendation methods based on download counts and hotness are used, then the software management system can provide a simple recommendation function, but the relevance between recommended software and specific user needs is low
Solution Approach 1:
The patent segments users into different user clusters based on their software usage behavior patterns. Instead of treating all users uniformly, the system divides the user base into distinct groups (e.g., game players, office workers, students) and provides tailored software recommendations for each cluster, thereby improving recommendation accuracy while maintaining system simplicity
Solution Approach 2:
The patent changes the recommendation parameters from simple download counts to complex user behavior patterns including software installation, usage frequency, and usage duration. By transforming the recommendation basis from static hotness metrics to dynamic user-specific parameters, the system achieves higher relevance without significantly increasing operational complexity
2Adaptability or versatility
If software repository integrates thousands of software across various categories, then users have more software choices, but it becomes difficult to recommend high-quality and targeted software
Solution Approach 1:
The patent applies local quality by providing different recommendation strategies for different user clusters. Instead of applying a uniform recommendation approach to all software, the system tailors the recommendation quality and selection criteria to each user group's specific needs and behavior patterns, thereby maintaining high recommendation accuracy across diverse software categories
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring user software usage behavior and using this information to refine future recommendations. The system collects data on which software users install, how frequently they use it, and how long they keep it running, then uses this feedback to improve the precision of software quality assessment and recommendation
Data Source
AI summary
A method and a system for processing recommended software are disclosed. A cluster analysis module performs a cluster analysis for users based on software using information reported by the users, and determines a software list corresponding to each user cluster, and sort software in the software list according to using condition of the software. A recommendation module determines a user cluster that is the most relevant to a specific user based on software using information of the specific user, and selects top N pieces of software from a software list corresponding to the user cluster to recommend the selected top N pieces of software to the specific user, where N is a predefined value.