User-Curated Media Topic Selection for Content Recommendation
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Solution Overview
Problem
Existing online media content sharing platforms fail to effectively recommend content to users interested in topics beyond those initially identified by content providers or algorithms, limiting the ability to share content with users who have different perspectives or interests.
Innovation Solution
A recommendation engine that allows users to select and associate custom topics with media content, enabling the content to be shared with users who follow those topics, based on the user's perspective, and tracks interactions to compute metrics for user expertise and content relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a media extraction engine uses an algorithm to associate topics based on keywords, then the content can be automatically categorized, but it fails to cover all aspects of the content including indirect associations
Solution Approach 1:
The patent segments the topic association process into two independent components: (1) automated algorithmic topic identification based on keywords, and (2) user-curated topic selection from a generated list. This segmentation allows each component to specialize - the algorithm handles obvious associations while users handle indirect associations, resolving the contradiction between automation efficiency and information completeness.
Solution Approach 2:
The patent introduces an intermediary layer between the automated extraction engine and the content distribution system. This intermediary presents a list of potential topics to users, who then select the most relevant ones. This intermediary mechanism bridges the gap between automated categorization and nuanced human judgment, preventing loss of indirect topic associations.
2Ease of operation
If users share content with users following algorithm-identified topics, then content distribution is simplified, but users cannot reach audiences interested in indirectly related topics
Solution Approach 1:
The patent makes the content sharing process dynamic by allowing users to adjust topic selections based on their intent. Instead of a fixed algorithmic approach, users can dynamically choose from multiple topics including indirect associations, enabling the system to adapt to different sharing scenarios and reach diverse audiences while maintaining ease of operation through automated list generation.
3Device complexity
If the system provides only algorithm-generated topic lists, then the interface is simple, but it cannot capture user perspective on content relevance
Solution Approach 1:
The patent applies preliminary action by having the system pre-generate a comprehensive list of potential topics using automated extraction before presenting them to users. This preliminary computational work reduces the user's task to simple selection rather than creation, maintaining interface simplicity while enabling precise user perspective expression through informed choice from the pre-prepared list.
Data Source
AI summary
Methods and systems for recommending online media content to other users includes receiving a selection of media content rendered on content page. The media content is identified by a user for sharing. A list of topics associated with the user is generated for presenting on a user interface. The topics are descriptive of the media content selected for sharing. Selection of one or more topics for the selected media content is received from the user. The received selections define the user's relevancy perspective for the selected media content. A recommendation for the selected media content is provided in content streams of users that follow the selected topics for users interactions with the selected media content.