Cloud Application Recommendation via Conditional Probability Tables
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Solution Overview
Problem
Users of cloud computing services face difficulties in selecting suitable applications for opening and editing proprietary or less common file types, as they are overwhelmed by numerous options and lack awareness of useful applications, making it hard to find the right tools among similar choices.
Innovation Solution
A cloud computing service builds and utilizes global and conditional probability tables to recommend applications based on installation patterns across all users, creating a scored list that suggests applications most frequently installed alongside the user's existing apps, thereby simplifying the selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud computing services offer a large number of applications to users, then the variety and usefulness of available tools increase, but users become overwhelmed with choices and find it difficult to select suitable applications
Solution Approach 1:
The system collects feedback data from multiple users about their application installation and usage patterns. This feedback is processed to generate probability tables that predict which applications a target user should install based on similarities with other users. The system continuously refines recommendations by incorporating new feedback, resolving the contradiction between offering many applications and making selection easy.
Solution Approach 2:
The patent introduces probability tables and scoring mechanisms as intermediaries between the large set of available applications and the user's selection process. These intermediaries process the overwhelming number of options by calculating conditional probabilities and generating scored lists, thereby mediating between application variety and selection ease.
2Reliability
If users manually install and test multiple applications to find suitable ones, then they may discover applications that meet their needs, but they waste significant time and effort in the process
Solution Approach 1:
The system performs preliminary actions by pre-calculating probability tables and scored lists of recommended applications before the user needs to make selections. By analyzing installation patterns of similar users in advance, the system prepares ready-to-use recommendations, eliminating the need for users to manually install and test multiple applications.
Solution Approach 2:
The patent copies successful application installation patterns from one user group to another by identifying users with similar characteristics and replicating their application selections. This copying of proven configurations reduces both time and effort while maintaining high reliability in finding suitable applications.
3Loss of information
If users are not aware of certain applications that may be useful, then they miss out on potentially valuable tools, but providing information about all available applications overwhelms them
Solution Approach 1:
Instead of providing uniform information about all applications to all users, the system applies local quality by tailoring recommendations to each user's specific characteristics, installed applications, and usage patterns. The probability tables and scored lists provide customized information locally relevant to each user, avoiding overwhelming them with irrelevant details about all available applications.
Data Source
AI summary
A method for providing a conditional scored list of applications for use in recommending applications includes storing on a cloud computing service a conditional probability table across a set of available applications provided by the cloud computing service. The cloud computing service receives a request to provide a scored list of applications for a user, retrieves a set of user-installed applications for the user, and calculates a total conditional probability for each application in the set of available applications. The cloud computing service then constructs the scored list of applications from the set of available applications, where a score of each application is its corresponding total conditional probability, and outputs the scored list of applications.