Interest Taxonomy Construction for Evolving User Content Signals
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Solution Overview
Problem
Conventional content understanding and curation techniques lack depth, granularity, adaptability, and comprehensive coverage, leading to ineffective matching of user interests due to static taxonomies and fragmented systems.
Innovation Solution
An interest graph is constructed using unsupervised machine learning to dynamically adapt to emerging interests, leveraging tailored preprocessing, unsupervised keyword extraction, and a pairwise classification model for content tagging, providing a granular and expansive taxonomy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional static taxonomies are used for content categorization, then system complexity is reduced, but adaptability to evolving user interests deteriorates
Solution Approach 1:
The patent implements a dynamic taxonomy system that automatically evolves with user interests through machine learning. The system continuously updates category structures and relationships based on analyzed user interactions, transforming static content categorization into an adaptive framework that grows and reorganizes itself according to emerging user preferences and behaviors.
Solution Approach 2:
The system employs unsupervised machine learning algorithms that automatically extract keywords, identify categories, and construct taxonomy structures without human intervention. The content understanding system self-organizes and self-updates by analyzing user interactions, eliminating the need for manual taxonomy maintenance while achieving continuous adaptation to evolving interests.
2Measurement precision
If comprehensive content analysis is performed to achieve deep content understanding, then content curation quality improves, but processing time increases
Solution Approach 1:
The system performs preliminary content analysis by pre-processing and indexing content features during ingestion. Keywords, metadata, and structural elements are extracted and stored in advance, enabling rapid retrieval and comparison during content curation operations without requiring complete re-analysis, thus reducing real-time processing delays.
Solution Approach 2:
The patent replaces traditional manual or rule-based content analysis with unsupervised machine learning models that automatically understand content semantics. These algorithms efficiently process and interpret content at scale, achieving deep understanding through pattern recognition and statistical analysis rather than exhaustive mechanical examination of each content element.
3Manufacturing precision
If manual taxonomy construction is used, then taxonomy accuracy improves, but productivity deteriorates
Solution Approach 1:
The system automatically constructs and maintains taxonomies through unsupervised learning algorithms that analyze content patterns and user behaviors. The machine learning model self-organizes category structures, extracts meaningful keywords, and establishes relationships between concepts without human intervention, achieving both accuracy through intelligent pattern recognition and high productivity through automation.
Solution Approach 2:
The patent dynamically adjusts taxonomy parameters such as category granularity, relationship weights, and hierarchical structures based on analyzed data patterns. The system modifies taxonomy characteristics in response to emerging content trends and user preferences, maintaining accuracy through adaptive parameter optimization while achieving rapid construction through algorithmic processes.
Data Source
AI summary
Techniques for creating an interest graph include obtaining content items from multiple content sources and applying tailored (e.g., source-specific) preprocessing to the content items based on their respective content source. Text is extracted and salient keywords and key phrases are identified using unsupervised machine learning models. The keywords and key phrases become nodes in an interest graph, each node comprising an embedding of a keyword or key phrase in a common embedding space, with edges representing semantic similarity based on embeddings or co-engagement patterns. The graph provides an expansive, granular, and dynamic taxonomy easily adaptable to emerging interests. The interest graph overcomes limitations of conventional taxonomies that lack depth, fail to capture niche interests, and cannot adapt to reflect evolving user preferences. The described techniques construct a rich interest graph from diverse content for improved content understanding.