Software Recommendation System Using User Personalization Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face difficulties in finding suitable software due to the vast number of options and complex classification systems on software download websites, leading to tedious search processes and wasted time.
Innovation Solution
A software recommending method and system that detects installed software and web applications, analyzes user personalization data such as age, personality, and gender, and provides tailored software recommendations, including adjustments to interface layout and display settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If software classification and download ranking are provided on dedicated software download websites, then software organization and accessibility are improved, but users still face difficulty in picking suitable software due to the vast number of options
Solution Approach 1:
The system automatically detects installed software and web applications on the user's client device, and autonomously analyzes usage patterns to generate personalized recommendations without requiring manual search or classification navigation by the user
Solution Approach 2:
The recommendation mechanism transforms the approach from static software classification to dynamic personalization by analyzing multiple parameters including usage frequency, installation patterns, and user behavior to generate context-aware software recommendations
2Ease of operation
If software classification becomes finer and more detailed, then software organization is improved, but users need to be very familiar with the classification system to find desired software quickly
Solution Approach 1:
The system performs self-analysis of the user's software ecosystem and automatically generates recommendations based on detected usage patterns, eliminating the need for users to navigate complex classification hierarchies
Solution Approach 2:
The system pre-analyzes installed software and usage patterns before the user needs to search, preparing personalized recommendations in advance based on detected user behavior and software installation data
3Reliability
If users manually search for desired software through dedicated websites, then they can find specific software, but it consumes a lot of time and involves tedious operation steps
Solution Approach 1:
The system automatically detects and analyzes the user's software installation and usage data to generate recommendations without requiring manual search operations
Solution Approach 2:
The system continuously monitors software usage patterns and provides feedback-based recommendations that adapt to changing user needs, improving recommendation accuracy over time through iterative learning from user behavior
Data Source
AI summary
Disclosed are a software recommending method and a software recommending system. The method comprises: detecting software already installed and/or web application already run on a client (101); analyzing software already installed and/or web application already run on the client, and obtaining personalization data of the client user, wherein the personalization data include age data, and/or personality data, and/or gender data (102); providing a corresponding software recommending mechanism according to the personalization data of the client user (103). This solution makes software recommendations for different personalities of different users, so that the recommendations are more targeted; and a user also does not need to search a variety of software for desired software through complicated operations.