Ranking Calls to Action by Relevance Confidence Scores
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Solution Overview
Problem
Conventional approaches for utilizing calls to action (CTAs) in social networking systems are inefficient, irrelevant, and lack interactivity, leading to reduced user engagement and experience when selecting appropriate CTAs for online resources.
Innovation Solution
A system and method for ranking CTAs based on information associated with online resources, using a model that evaluates relevance through confidence scores, incorporating signals such as category, location, popularity, and historical behavior, and dynamically updating with machine learning processes to prioritize and recommend the most relevant CTAs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional approaches are used to present calls to action on social networking pages, then implementation is simple, but the calls to action become irrelevant and reduce user engagement
Solution Approach 1:
The system pre-processes and stores information about pages and calls to action in databases before user interaction. Confidence scores and relevance data are calculated in advance, so that when a user views a page, the most relevant CTAs are already identified and can be presented immediately without complex real-time computation.
Solution Approach 2:
The patent introduces intermediate components including a data store for storing page and CTA information, a model for generating confidence scores, and a ranking system that mediates between available CTAs and user needs. These intermediaries translate raw data into ranked, relevant recommendations without requiring direct complex analysis at interaction time.
2Ease of operation
If limited functionality is provided on social networking pages, then system complexity is reduced, but user engagement and interaction are diminished
Solution Approach 1:
The system automatically performs data collection, processing, and CTA ranking without requiring manual intervention. The model self-updates confidence scores based on stored information and user interactions, and the system automatically presents relevant CTAs to users, reducing the need for complex manual configuration while enhancing user experience.
3Reliability
If calls to action are not dynamically updated, then system maintenance is simpler, but relevance to user behavior deteriorates
Solution Approach 1:
The system incorporates feedback loops where user interactions with CTAs and pages are collected and stored. The model uses this feedback to update confidence scores and refine relevance calculations over time. This automatic feedback mechanism ensures CTAs remain relevant to current user behavior without requiring manual updates, maintaining reliability while using systematic rather than ad-hoc complexity.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can identify a page within a social networking system. Information associated with at least one of the page or a representative of the page can be acquired. A set of calls to action implementable at the page can be identified. The set of calls to action can be ranked based on the information associated with at least one of the page or the representative of the page.