Personalized Term Expansion via Co-occurrence Analysis
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Solution Overview
Problem
Users often fail to accurately annotate multimedia content in content sharing systems, leading to inefficiencies in searching and categorizing content items.
Innovation Solution
A method for automatically generating recommended terms based on initial sets of annotations or query terms by identifying co-occurrences across various contexts, including global and personal contexts, to suggest additional annotations or query terms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually annotate content items, then annotation accuracy can be maintained, but user time and effort are significantly consumed
Solution Approach 1:
The system performs preliminary annotation by analyzing co-occurrence patterns from previously annotated content items. When a user annotates a new content item, the system has already prepared recommended terms based on historical data, so the user can quickly select from pre-computed suggestions rather than creating annotations from scratch.
Solution Approach 2:
The system enables self-service annotation by automatically generating term recommendations based on co-occurrence analysis of user behavior patterns. The annotation system serves itself by learning from user annotations and automatically providing relevant term suggestions, reducing the need for manual effort while maintaining accuracy.
2Loss of time
If users annotate content items with minimal effort, then user time is reduced, but annotation accuracy and relevance deteriorate
Solution Approach 1:
The system implements feedback by continuously analyzing user annotation behavior and using co-occurrence patterns to refine term recommendations. When users select or reject recommended terms, this feedback is incorporated into the co-occurrence analysis, improving the accuracy of future recommendations while keeping user effort minimal.
Solution Approach 2:
The annotation system provides self-service by automatically generating context-relevant term recommendations based on learned user preferences and co-occurrence patterns. Users simply need to review and select from pre-generated suggestions rather than creating annotations manually, maintaining both speed and accuracy.
3Measurement precision
If the system provides personalized term recommendations, then user satisfaction and annotation quality improve, but system complexity increases
Solution Approach 1:
The system segments the recommendation process into distinct components: co-occurrence analysis module, context identification module, and term generation module. Each component handles a specific aspect of the recommendation process, making the overall complex system manageable through modular design while still providing personalized recommendations.
Solution Approach 2:
The co-occurrence analysis system serves multiple functions: it analyzes user annotation patterns, generates term recommendations, identifies contexts, and adapts to different user preferences. This multi-functionality reduces the need for separate specialized modules, managing system complexity while maintaining personalized recommendation capabilities.
4Measurement precision
If the system analyzes multiple contexts for term recommendations, then recommendation relevance improves, but processing time increases
Solution Approach 1:
The system performs preliminary analysis of co-occurrence patterns across multiple contexts in advance, building indexes and statistical models before they are needed for actual recommendations. When generating recommendations, the system queries pre-computed data structures rather than performing full context analysis in real-time, reducing processing time while maintaining multi-context relevance.
Data Source
AI summary
Disclosed are methods and apparatus for providing a set of recommended annotations. A set of tags associated with a content item within a content sharing system is identified, where the content item is associated with a user of the content sharing system. A set of global tag co-occurrences within the content sharing system is identified for the set of tags, where each of the global tag co-occurrences in the set of global tag co-occurrences identifies a pair of tags including one of the set of tags, wherein the pair of tags have both been used together to tag at least one of the content items within the content sharing system. One or more sets of personal tag co-occurrences is identified for the set of tags, where each of the sets of personal tag co-occurrences is used in a corresponding one of one or more personal contexts of the content sharing system, the one or more personal contexts relating to the user of the content sharing system. Each of the personal tag co-occurrences in the sets of personal tag co-occurrences identifies a pair of tags including one of the set of tags that have both been used together to tag at least one content item within a corresponding one of the one or more personal contexts. A set of recommended tags is generated from the set of global tag co-occurrences and the one or more sets of personal tag co-occurrences.