Content Suggestion Engine Using User Folder Copresence
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Solution Overview
Problem
Conventional search engines rely on keyword matching and automated algorithms, which fail to accurately identify semantically relevant content due to the complexity of natural language and lack of semantic analysis, leading to poor search results and inefficient content retrieval.
Innovation Solution
A system that utilizes crowd-sourced techniques by allowing users to organize and categorize content items into folders, with an automated suggestion engine providing semantically related content based on user interactions and folder associations, leveraging copresence and copresence counts to suggest relevant items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional search engines use automated keyword matching algorithms, then search operations can be performed automatically without human intervention, but the accuracy of identifying semantically relevant content deteriorates due to the complexity of natural language
Solution Approach 1:
The patent introduces an intermediary mechanism (crowd-sourced user feedback and folder organization data) between the automated search system and the semantic meaning of content. Instead of relying solely on automated algorithms to understand semantics, the system uses user-generated organizational structures as a mediator to bridge the gap between automated processing and semantic accuracy.
Solution Approach 2:
The system implements feedback loops where user interactions (organizing content into folders, copresence patterns) continuously inform and refine the suggestion engine. This feedback mechanism allows the system to learn semantic relationships from actual user behavior rather than relying on imperfect automated analysis.
2Device complexity
If search engines rely solely on automated algorithms without crowd-sourcing, then the system complexity remains low, but the ability to derive semantic meaning from content deteriorates
Solution Approach 1:
The system enables self-service by allowing users to automatically contribute semantic organization data through their natural content organization activities. Users inadvertently perform semantic annotation by organizing content into folders, and this self-generated data is harvested to improve the suggestion engine without requiring manual intervention from the system.
Solution Approach 2:
The patent merges multiple data sources (user folder organizations, copresence patterns, search queries) into a unified semantic understanding model. By combining these diverse inputs, the system achieves comprehensive semantic coverage that exceeds what any single automated algorithm could provide alone.
3Productivity
If conventional search engines use simple keyword matching, then the search process is fast and efficient, but the retrieval of semantically related content deteriorates
Solution Approach 1:
The system performs preliminary semantic organization through crowd-sourced folder creations and content associations before search queries are executed. By pre-organizing content based on user behaviors and copresence patterns, the system prepares semantic structures in advance that enable fast yet accurate retrieval during actual search operations.
Data Source
AI summary
A computer-implemented content suggestion engine provides content suggestions to a requesting user based on information about content items that other users may have independently categorized or organized into folders within a content repository. Embodiments of the method comprise a content repository having a plurality of content items, where each content item is associated with one or more user-created folders. Embodiments further comprise receiving, via a network, a suggestion request for suggested content, where the suggestion request identifies a first content item for which suggestions are sought. Other content items in the content repository are then identified as potential suggestions based on the application of a formal relationship between the first content item and the potential suggested content items. One or more of the potential suggested content items may then be provided in response to the suggestion request via the network.