Dynamic Application Recommendation via Interaction Graphs
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Solution Overview
Problem
Users face difficulties in identifying the most relevant third-party applications from a large corpus for interacting with specific content objects, as existing mechanisms are inadequate in recommending applications based on user and content object context.
Innovation Solution
The system analyzes historical application usage activity to dynamically recommend applications by constructing application activity graphs, considering user interactions, permissions, and scoring to present the most relevant applications to users through a native application interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed set of third-party applications is presented to users, then the system complexity is reduced, but the relevance and usefulness of applications to specific user interactions deteriorates
Solution Approach 1:
The patent implements dynamic application recommendation by analyzing user interaction patterns, content object types, and collaboration contexts to generate context-specific application suggestions. The system continuously adapts recommendations based on observed usage patterns rather than presenting a static fixed set, resolving the contradiction between system simplicity and application relevance.
Solution Approach 2:
The system automatically analyzes user behavior patterns and interaction data to generate application recommendations without requiring manual user input or configuration. The recommendation engine self-adjusts based on observed usage patterns, content types, and collaboration contexts, providing relevant applications while maintaining system simplicity through automated decision-making.
2Adaptability or versatility
If users are presented with hundreds of available third-party applications, then the adaptability and choice are improved, but the time required to find relevant applications and user effort increases
Solution Approach 1:
The patent extracts and highlights only the most relevant third-party applications from the large corpus of available applications based on analyzed user patterns and interaction contexts. Instead of presenting all hundreds of applications, the system extracts and prioritizes a small subset that is most likely to be useful, dramatically reducing the time users need to spend searching while maintaining access to the full application ecosystem.
Solution Approach 2:
The system performs preliminary analysis of user interaction patterns, content object types, and collaboration contexts before users need to select applications. By pre-computing and caching recommendation data based on observed patterns, the system prepares relevant application suggestions in advance, eliminating the need for users to manually search through hundreds of applications at the moment of need.
3Ease of operation
If simplistic mechanisms like associating applications with file types are used, then the ease of operation is improved, but the precision and accuracy of recommendations deteriorates
Solution Approach 1:
The patent implements local quality by providing different levels of recommendation precision for different contexts. For simple file-type-based interactions, basic associations suffice, while for complex collaboration scenarios involving multiple users, content types, and interaction patterns, the system applies more sophisticated analysis. This localized approach to recommendation precision maintains ease of operation for simple cases while improving accuracy for complex cases.
Solution Approach 2:
The system dynamically adjusts recommendation parameters based on the complexity and context of the interaction. For simple file operations, basic file-type associations are used, but for complex collaboration scenarios involving multiple users, content types, and interaction patterns, the system activates more sophisticated analysis parameters including user behavior patterns, collaboration history, and contextual relevance, thereby improving precision without sacrificing simplicity for basic operations.
Data Source
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AI summary
Methods, systems and computer program products for shared content management systems. In a content management system that supports multiple applications that operate on shared documents, multiple modules are operatively interconnected to make and present activity-based application recommendations. Techniques for making activity-based application recommendations include recording a series of interaction events from multiple users, which events correspond to a series of interactions performed by a plurality of applications over a shared content object. Constituent interaction events from the series of interactions are analyzed to determine a set of recommended applications. The set of recommended applications is presented to a user in a dynamically-populated user interface.