Third-Party Information Prioritization for Relevant Item Presentation
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Solution Overview
Problem
Existing cloud-based service platforms face inefficiencies in presenting information items selected by third-party platforms, often leading to irrelevant content that users do not engage with, due to the lack of dynamic and effective user interaction management.
Innovation Solution
A system and method that utilizes a processor to receive requests, select target item types based on predefined criteria, and generate an ordered list of boosting information items by consolidating item scores, enhancing user interaction and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If third-party platforms independently select candidate information items for users, then platform control and autonomy are maintained, but information relevance and user engagement deteriorate due to irrelevant recommendations
Solution Approach 1:
The patent introduces a cloud-based service platform as an intermediary between third-party platforms and users. This intermediary receives candidate information items from third-party platforms, scores them based on user preferences and historical data, and selects the most relevant items for presentation. This resolves the contradiction by maintaining third-party platform autonomy while ensuring information relevance through centralized intelligent mediation.
Solution Approach 2:
The system implements feedback mechanisms where user interactions with information items (clicks, selections, engagements) are continuously monitored and fed back to the scoring model. This feedback loop allows the system to dynamically adjust item scores and recommendations, improving information relevance over time while preserving third-party platform control over the recommendation process.
2Quantity of substance
If cloud-based service platform provides comprehensive candidate information items to third-party platforms, then information coverage is improved, but processing complexity and computational resources increase
Solution Approach 1:
The patent extracts only the essential scoring and selection functionality from the complex cloud-based platform, implementing it as a streamlined service that third-party platforms can consume. This extraction approach maintains comprehensive information coverage by utilizing the full candidate pool while reducing processing complexity through focused, modular scoring algorithms that can be efficiently integrated by third-party systems.
Solution Approach 2:
The system segments the information recommendation process into distinct modules: candidate generation by third-party platforms, scoring by the cloud-based service, and final selection by the platform. This segmentation allows comprehensive information coverage at each stage while distributing computational complexity across multiple independent components, reducing the burden on any single system.
3Quantity of substance
If all candidate information items are presented to users, then information completeness is maintained, but user attention and engagement decrease due to information overload
Solution Approach 1:
The patent applies local quality by differentiating the treatment of individual information items based on their scored relevance to specific users. Instead of uniform presentation, the system assigns different priorities, positions, and visibility levels to items based on their calculated scores. This ensures information completeness is maintained in the pool of candidates while optimizing user engagement by strategically presenting the most relevant items first.
Solution Approach 2:
The system performs preliminary scoring and ranking of all candidate information items before they are presented to users. By pre-processing and organizing items according to their relevance scores, the system maintains complete information availability while ensuring that users encounter the most engaging and relevant content first, thereby optimizing attention and engagement without sacrificing completeness.
Data Source
AI summary
This application is directed to systems and methods for organizing information. In some embodiments, a disclosed method includes receiving a plurality of requests associated with a plurality of items corresponding to a plurality of item types, where each item type includes one or more items; selecting, from the plurality of item types, a subset of target item types that satisfy a predefined type selection criterion associated with the plurality of requests; for each item type of the subset of target item types, selecting a set of boosting items from a set of target items of the respective target item type based on an item score of each of the set of target items; and generating an ordered list of boosting information items for the subset of target item types by consolidating the sets of boosting items of the subset of target item types.