Interactable Comment Elements for Media Recommendation Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current graphical user interfaces for media assets face challenges in providing relevant recommendations, as they often rely on incomplete metadata and do not effectively utilize user interactions, such as comments, to enhance recommendation accuracy and user experience.
Innovation Solution
The system analyzes text comments to identify interactable elements, incorporating metadata from both the media asset and the viewing device, and modifies the comment interface to include hyperlinks and supplemental content, allowing for improved search functionality and recommendation generation based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recommendations are provided based on metadata and popularity only, then recommendation generation is simple, but recommendation accuracy is low because user interaction data like comments are not utilized
Solution Approach 1:
The patent merges multiple data sources including media asset metadata, device metadata, comment text data, and popularity metrics into a unified recommendation generation system. This combination allows the system to leverage user interaction data from comments while maintaining the existing metadata-based recommendation framework, thereby improving recommendation accuracy without requiring a complete system overhaul
Solution Approach 2:
The system performs preliminary text analysis on comments to identify interactable elements and generate search results before the user even requests recommendations. By pre-processing comment data and creating searchable indexes of user-generated content, the system prepares recommendation data in advance, reducing computational load during actual recommendation generation while improving accuracy
2Ease of operation
If all available media assets are provided as recommendations, then no filtering is needed, but the interface becomes cluttered and user experience deteriorates
Solution Approach 1:
The patent applies local quality by making specific portions of comment text interactable rather than treating all text uniformly. By identifying and highlighting key terms, entities, and phrases within comments that are relevant to the current media asset, the system creates focused, context-relevant recommendations while keeping the interface clean and uncluttered. Only the most relevant comment portions are transformed into interactable recommendation elements
Solution Approach 2:
The system extracts meaningful information from comment text by identifying interactable elements such as keywords, entities, and phrases that are relevant to the media asset. This extraction process separates useful recommendation data from the bulk of comment text, allowing the interface to display only the most relevant recommendations derived from comments rather than all possible media assets
3Measurement precision
If comment text is analyzed to generate search results, then recommendation relevance improves, but computational load and processing time increase
Solution Approach 1:
The system performs preliminary text analysis on comments to identify interactable elements, extract keywords, and generate search indexes in advance. By pre-processing comment data and creating searchable structures before they are needed for recommendation generation, the system reduces computational load during actual user interactions while maintaining high recommendation relevance
Solution Approach 2:
The patent applies partial action by analyzing only the necessary portions of comment text rather than processing entire comments uniformly. The system identifies and processes only the interactable elements within comments that are relevant to the current media asset context, avoiding unnecessary computational overhead while maintaining recommendation quality
Data Source
AI summary
Systems and methods for improving displays of media assets are disclosed herein. In an embodiment, a system receives a plurality of text comments from a plurality of devices to which a media asset was transmitted. The system analyzes the comments to identify text strings within the text comments. The system generates interactable elements from the text strings in the text comments, such that an interaction with the text string causes display of identifiers of media assets corresponding to the text string.