Widget Recommendation via Co-occurrence Matrix for Webinar Engagement
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Solution Overview
Problem
Existing solutions fail to effectively recommend user interface widgets for landing pages and webinars, crucial for engaging audiences, as they primarily focus on viewer perspectives rather than providing optimal widget combinations for high engagement.
Innovation Solution
A system and method that uses data extraction, feature vectors, co-occurrence matrices, and collaborative filtering to recommend user interface widgets based on past engagement scores, ensuring the selection of widgets that enhance audience interaction and engagement for presentations and webinars.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional content recommendation approaches (Aimojo, Kindred Posts) are used, then user engagement time increases, but they are not suitable for recommending user interface widgets for landing pages and webinars
Solution Approach 1:
The patent applies copying by creating a co-occurrence matrix that replicates successful widget combination patterns from historical webinar data. Instead of developing complex new recommendation logic, the system copies proven widget pairings and configurations that have previously achieved high engagement scores, applying these patterns to new webinar contexts through collaborative filtering techniques
Solution Approach 2:
The system changes parameters by transforming raw webinar interaction data into structured feature vectors, then converting these into co-occurrence probability matrices. This parameter transformation enables the system to recommend widgets based on statistical patterns rather than complex rule-based logic, simplifying the recommendation engine while improving adaptability
2Reliability
If presenters manually select widgets for landing pages, then implementation is simple, but engagement optimization is insufficient because presenters cannot determine which widget combinations render the best engagement
Solution Approach 1:
The system implements feedback by continuously analyzing webinar engagement metrics (chat activity, poll responses, time spent) and using this feedback to update the co-occurrence matrix. This creates a closed-loop system where past webinar performance data feeds back into improving future widget recommendations, progressively optimizing engagement without requiring complex manual adjustments
Solution Approach 2:
The recommendation system performs self-service by automatically generating widget recommendations based on historical data patterns, eliminating the need for presenters to manually research or test different widget combinations. The system serves itself by using its own accumulated webinar data to improve its recommendations over time through the co-occurrence matrix
3Productivity
If only one chance to catch audience attention is available, then the presentation impact must be maximized, but determining optimal widget combinations becomes more difficult
Solution Approach 1:
The system applies preliminary action by pre-calculating and storing successful widget combination patterns in the co-occurrence matrix before new webinars begin. By analyzing historical data in advance and capturing proven engagement patterns, the system prepares ready-to-deploy recommendations that can be immediately applied to maximize the first impression and initial audience engagement
Data Source
AI summary
A widget recommendation system and method recommends user interface widgets for an event that contains content or a presentation. In one embodiment, the system recommends user interface widgets for a landing page for the content or presentation of an event. The system and method may extract features from past events and recommend the user interface widgets.