App Ranking via Social Sentiment and Reliability Ratings
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Solution Overview
Problem
Current mobile application search techniques lack effective ranking methodologies, as applications do not typically link to each other, and user sentiment on social media can be manipulated, making it difficult to provide relevant search results.
Innovation Solution
An application distribution system analyzes social media posts to determine sentiment and reliability ratings for application authors, combining these ratings to rank applications based on their relevance and reliability, thereby providing a more accurate search result list to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user sentiment on social media is used to rank applications, then application ranking relevance is improved, but susceptibility to manipulation increases
Solution Approach 1:
The patent introduces an intermediary entity (trusted user profile) between the raw social media sentiment and the application ranking system. This intermediary validates and filters sentiment expressions, allowing the system to utilize user feedback while protecting against manipulation. The intermediary acts as a buffer that preserves the useful information (genuine user sentiment) while blocking harmful elements (manipulative posts).
Solution Approach 2:
The system dynamically changes the parameters used for ranking by considering multiple factors beyond simple sentiment count, including the trusted profile status of users, the relationship between users, and the context of posts. This multi-parameter approach transforms the ranking methodology from a single-metric system (prone to manipulation) to a complex multi-factor system that is more resistant to manipulation while maintaining relevance.
2Measurement precision
If social media posts are analyzed for sentiment, then application relevance assessment is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex task of application ranking into multiple independent components: sentiment analysis, user profile trustworthiness assessment, relationship analysis, and final ranking computation. Each component can be developed, tested, and maintained independently, reducing overall system complexity while achieving high measurement precision through the integration of these specialized modules.
3Measurement precision
If traditional link-based ranking is applied to applications, then search result relevance is improved, but applicability deteriorates
Solution Approach 1:
The patent inverts the traditional web ranking approach by instead of having applications link to each other, having users link to applications through social media profiles and relationships. This inversion adapts the proven link-based ranking methodology to the mobile application context where applications do not naturally link to each other, but users do have social connections that can indicate application relevance.
Data Source
AI summary
According to an implementation, an application distribution system may receive a search query from a user and generate indicators of a set of applications based on the search query. The system may determine an influence rating for an entity that provided social media posts associated with one of the applications. The system may determine a sentiment rating for the content of the posts and determine a reliability rating for the entity. The reliability rating may be based the number of posts and the number of the entity's social media relationships. The system may determine an application rating for the application based on the influence rating, the sentiment rating, and the reliability rating. The system may rank the application within a list of the set of applications based on the application rating and provide the list to the device associated with the user.