Contextual Presentation Recommendation System
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Solution Overview
Problem
Users face difficulty in finding relevant and interesting electronic presentations among a large repository, as manually searching through thousands of presentations is time-consuming and laborious, often resulting in missed opportunities for engaging or relevant content.
Innovation Solution
A system and method that recommend electronic presentations based on contextual, behavioral, and user profile data, extracting features from viewed presentations and utilizing social networking integration to suggest similar or relevant content, thereby streamlining the discovery process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually search through a large repository of electronic presentations, then they can find relevant content, but the process becomes time-consuming and laborious
Solution Approach 1:
The patent introduces a recommendation system as an intermediary between users and the presentation repository. This system analyzes user profiles, viewing history, and presentation metadata to automatically generate personalized recommendations, eliminating the need for users to manually search through thousands of presentations while ensuring they receive relevant content matched to their interests and needs
Solution Approach 2:
The system implements feedback mechanisms by tracking user viewing behavior, engagement metrics, and interaction patterns. This feedback is continuously used to refine and personalize recommendation algorithms, improving the accuracy of presented content over time while reducing the effort users must invest in finding relevant material
2Quantity of substance
If the repository contains thousands of electronic presentations, then more relevant content is available, but it becomes difficult for users to find interesting presentations
Solution Approach 1:
The patent applies local quality by customizing the presentation selection process for each individual user based on their unique profile characteristics, viewing history, and demonstrated preferences. Rather than applying a uniform approach to all users, the system tailors recommendations to match each user's specific interests, professional background, and engagement patterns, making the vast repository accessible through personalized content curation
Solution Approach 2:
The system dynamically changes multiple parameters including user profile attributes, behavioral metrics, presentation metadata weights, and recommendation algorithm configurations. These parameter adjustments allow the system to adapt to changing user preferences and optimize the matching between users and presentations, transforming the overwhelming task of browsing thousands of items into a streamlined personalized recommendation process
Data Source
AI summary
Systems and methods are disclosed that recommend one or more electronic presentations to a user based on one or more factors. These factors may include contextual information, behavioral information, profile information, or combinations of the foregoing. Contextual information may include content and/or features extracted from a given electronic presentation. Behavioral information may include user behavioral data, such as the number of times a user has viewed a presentation, the amount of the presentation viewed by the user, presentations previously viewed by the user, and other such behavioral data. Profile information may include user professional profile information, such as skills the user has identified as possessing, employment history information, and other such user professional profile information.