Crowdsourcing Descriptor Selection for Social Network Posts
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Solution Overview
Problem
Current methods for sharing internet resources on social networks lack efficient descriptor selection, leading to irrelevant or inappropriate content being shared, as they do not effectively utilize user preferences and historical selection data to prioritize descriptors.
Innovation Solution
A method and system for crowdsourcing descriptor selection, where descriptors are identified from internet resources, prioritized based on popularity metrics and user features, and updated based on user selections, allowing users to customize posts with relevant content that aligns with their preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional methods are used for sharing internet resources on social networks, then the sharing process is simple and quick, but the descriptors selected are often irrelevant or inappropriate and do not align with user preferences
Solution Approach 1:
The system pre-processes internet resources to extract multiple candidate descriptors before the user sharing moment. When a user initiates a share, the descriptors are already prepared and prioritized based on popularity metrics and user features, eliminating the need for complex real-time analysis and enabling accurate descriptor selection without adding significant complexity to the sharing process
Solution Approach 2:
The system implements feedback loops where user selections and interactions with descriptors are continuously monitored. This feedback is used to update popularity metrics and refine the prioritization of descriptors for each user, improving descriptor selection accuracy over time while maintaining a manageable system complexity through iterative refinement rather than complex upfront design
2Reliability
If multiple descriptors are extracted from internet resources, then the relevance of shared content improves, but the complexity of selecting and presenting descriptors increases
Solution Approach 1:
Multiple descriptors are extracted and prioritized in advance based on popularity metrics and user-specific features. When the user needs to share content, the descriptors are already ranked by relevance, so the user simply needs to review a pre-ordered list rather than manually evaluate multiple unsorted options, maintaining ease of operation while improving content relevance
Solution Approach 2:
The descriptor presentation is dynamically adapted to each user based on their profile, historical selections, and preferences. The system adjusts which descriptors are shown and in what order, making the interface personalized and intuitive for each user while handling multiple descriptors efficiently, thus improving relevance without significantly increasing operational complexity
3Measurement precision
If user preferences and historical data are utilized to prioritize descriptors, then the accuracy of descriptor selection improves, but the processing time and computational resources increase
Solution Approach 1:
User profiles, preferences, and historical selection data are pre-processed and stored in an optimized format before needed for descriptor prioritization. PopularitY metrics and user features are pre-calculated and maintained in data structures that enable rapid querying, reducing the computational burden at the moment of sharing while maintaining high prioritization accuracy
Solution Approach 2:
The system uses approximate or pre-computed popularity metrics and user feature matchings rather than performing exhaustive real-time analysis. This partial action approach provides sufficiently accurate descriptor prioritization without the full computational cost of complete analysis, reducing processing time while maintaining acceptable accuracy levels for the application context
4Adaptability or versatility
If descriptor popularity metrics are updated based on user selections, then the system adapts to user preferences over time, but the complexity of tracking and updating metrics increases
Solution Approach 1:
The system implements straightforward feedback mechanisms where user descriptor selections are tracked and used to incrementally update popularity metrics. This feedback loop enables the system to adapt to user preferences over time through simple counting and averaging operations rather than complex algorithms, improving adaptability while maintaining manageable complexity through iterative statistical refinement
Data Source
AI summary
Implementations disclose crowdsourcing descriptor selection. A method includes receiving, by a processor, a reference to an internet resource from a device, wherein the reference to the internet resource is a uniform resource locator (URL) provided in a post interface, wherein the post interface presents posts to a social network, determining, by the processor, a plurality of descriptors indicative of content within the internet resource, sending, by the processor, the plurality of descriptors to the device for presentation in the post interface according to a priority order, receiving, via the post interface a selection of one of the plurality of descriptors, and generating, by the processor, a post to the social network, the generated post comprising the selected one of the plurality of descriptors.