Personalized App Store Recommendations via User Profile Segmentation
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Solution Overview
Problem
The abundance of software applications in app stores makes it difficult for users to navigate and find relevant, undiscovered applications that match their interests, due to limited screen real estate and attention span.
Innovation Solution
A method for providing personalized software application recommendations by generating and analyzing user and software application profiles, using data such as user interactions, derived information, and metadata, to recommend relevant applications to users based on their preferences and behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the app store includes a substantial number of software applications to enhance flexibility and options, then the variety and options for users are improved, but the difficulty for users to navigate and identify relevant applications increases
Solution Approach 1:
The patent segments the large set of software applications into different categories and uses personalized filtering to divide the list into manageable portions. The system creates personalized lists by segmenting applications based on user preferences, making the overwhelming number of applications organized and navigable through categorical and preference-based grouping.
Solution Approach 2:
The patent introduces an intermediary system that acts as a mediator between the user and the applications. This intermediary includes personalized recommendation engines, filters, and categorization systems that process the large application database and present only relevant applications to users, reducing the cognitive load and navigation difficulty.
2Quantity of substance
If the app store displays all software applications to ensure comprehensive coverage, then the completeness of application selection is improved, but the user attention span and screen real estate are insufficient to review all applications
Solution Approach 1:
The patent applies preliminary action by pre-processing and pre-ranking applications based on user profiles, historical data, and predicted preferences before the user needs to make selections. The system performs this filtering and ranking in advance, so when users access the store, they immediately see pre-sorted, relevant applications rather than having to review everything from scratch.
Solution Approach 2:
The patent applies local quality by tailoring the presentation and ordering of applications to individual user characteristics and preferences. Instead of a uniform display for all users, the system creates localized, personalized views that highlight applications most relevant to each specific user's interests, usage patterns, and demographics.
3Area of stationary object
If the app store randomly excludes a considerable portion of undiscovered applications during distillation, then the screen real estate and user attention are conserved, but the likelihood of including relevant applications that the user might deem relevant is reduced
Solution Approach 1:
The patent implements feedback mechanisms that continuously learn from user interactions, downloads, ratings, and usage patterns. This feedback loop allows the system to refine its understanding of user preferences over time, improving the accuracy of application selection and reducing the likelihood of excluding relevant applications while maintaining efficient screen real estate usage.
Solution Approach 2:
The patent changes the parameters of application selection from random exclusion to preference-based filtering. Instead of arbitrarily excluding applications, the system adjusts selection criteria based on user profiles, engagement history, and predicted interest, ensuring higher relevance in the displayed applications while maintaining compact presentation.
Data Source
AI summary
Disclosed herein is a technique for providing software application recommendations to a user of a computing device. The technique can include: (1) receiving, from the computing device, a request for at least one software application recommendation, (2) identifying, among a plurality of user profiles, a user profile associated with the user, (3) accessing a plurality of software application profiles (SAPs), wherein each SAP of the plurality of SAPs is associated with a respective software application managed by the server computing device, (4) analyzing the user profile against a subset of the plurality of SAPs to identify, among the respective software applications associated with the subset of the plurality of SAPs, at least one software application to recommend, (5) associating the at least one software application recommendation with the at least one software application, and (6) causing the computing device to display the at least one software application recommendation.