Media Knowledge Graph Updates from Casual User Communications
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Solution Overview
Problem
Traditional media guidance applications fail to utilize casual references to media assets in user communications for personalized recommendations.
Innovation Solution
A media guidance application updates a knowledge graph based on user communications, isolating terms from casual conversations and confirming or modifying associations with candidate components using user input to enhance recommendation relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional media guidance applications base recommendations only on user's known interests, then the recommendation system is simple to implement, but the relevance of recommendations to user's actual preferences is reduced
Solution Approach 1:
The system performs preliminary action by proactively monitoring and analyzing user communications before users explicitly indicate their interests. The knowledge graph is continuously updated with entities and relationships extracted from casual conversations, social media posts, and other communication channels, preparing the system to provide more relevant recommendations without waiting for explicit user input.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary layer between user communications and the recommendation engine. This knowledge graph stores structured information about entities (people, places, things) and their relationships, serving as a mediator that transforms unstructured communication data into actionable recommendation insights, thereby improving recommendation relevance while managing system complexity.
2Measurement precision
If the system monitors and analyzes user communications to extract preferences, then recommendation relevance is improved, but user privacy concerns and data processing requirements increase
Solution Approach 1:
The system applies the extraction principle by selectively extracting only the necessary entities and relationships from user communications that are relevant to media recommendations. Rather than processing entire communication texts, the system identifies and extracts specific entities (movies, TV shows, celebrities, locations) and their relationships, reducing data processing complexity while maintaining user preference accuracy.
Solution Approach 2:
The knowledge graph implementation applies local quality by organizing data with different levels of detail and importance. Not all entities are treated equally - the system maintains detailed information about entities relevant to the user's interests while using more generalized representations for less relevant entities, optimizing data processing efficiency while preserving user preference accuracy.
Data Source
AI summary
Methods and systems are disclosed herein for updating a knowledge graph based on a user confirmation. A media guidance application receives a user communication and isolates a term of the user communication. The media guidance application identifies a candidate component of a knowledge graph associated with the term. The media guidance application requests user input directed to confirming whether the term is associated with the candidate component. In response to receiving the user input, the media guidance application modifies a strength of association between the term and the component.