Conversation-Weighted Media Search Result Updating
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Solution Overview
Problem
Existing media guidance systems struggle to effectively prioritize and integrate multiple viewer opinions in conversations to refine search results, leading to irrelevant or uninteresting media recommendations.
Innovation Solution
A media guidance application that utilizes a home assistant device to listen to viewer conversations, analyze speech patterns, and adjust search results based on user preferences and priorities, updating the search results iteratively to align with the most relevant viewer's input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all viewer comments and opinions in a conversation are used to update search results, then comprehensive user feedback is captured, but the results become less relevant and interesting because opinions may differ and conflict
Solution Approach 1:
The system introduces a conversation analysis module as an intermediary that processes and filters user feedback before updating search results. This module identifies the prioritized viewer and extracts only their relevant comments, acting as a mediator between multiple user opinions and the search result update mechanism, thereby maintaining result relevance while still incorporating user feedback.
Solution Approach 2:
The system applies different processing rules to different parts of the conversation data. Specifically, it identifies and prioritizes comments from the prioritized viewer while filtering or weighting comments from other viewers differently. This local differentiation in processing quality ensures that the most relevant user feedback drives the search result updates.
2Measurement precision
If the system monitors and analyzes conversations in real-time to update search results iteratively, then search result quality improves through continuous feedback, but system complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary identification of the prioritized viewer and their preferences before the conversation unfolds. By pre-establishing which viewer's opinions should carry more weight and what their preferred media categories are, the system reduces the complexity of real-time analysis during the actual conversation, as it only needs to filter and apply the prioritized viewer's comments rather than analyzing all participants equally.
3Measurement precision
If the system integrates user profiles and preferences with conversation analysis to fine-tune search results, then result relevance to individual users improves, but processing time and computational resources increase
Solution Approach 1:
The system pre-loads and stores user profile information including preferred media categories, genres, and viewing histories before conversation analysis begins. This preliminary preparation allows the conversation analysis module to quickly match extracted comments against preorganized user preferences without performing complex computations during real-time processing, thereby reducing processing time while maintaining personalization quality.
Data Source
AI summary
Systems and methods are described herein for updating search results based on a user's comment or a conversation among users using a media guidance application. A set of search results may be presented to a user or users. Comments or a conversation about the search results may be analyzed by the media guidance application. Selected comments by a user determined to have a greater weight may be used to update the search results.