Hierarchical Topic Modeling for Diverse Content Exploration
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Solution Overview
Problem
Current search paradigms are inefficient for exploratory searches where user intent is unclear or broad, leading to time-consuming and cumbersome processes, especially in content marketing and information exploration, as they rely on linear relevance-based results and fail to facilitate discovery of new topics and ideas.
Innovation Solution
The implementation of topic modeling algorithms and hierarchical categories allows for exploratory searching by grouping documents semantically, enabling users to navigate through a broader view of results and discover new concepts, rather than just retrieving highly relevant information, using natural language queries and unsupervised category inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current search paradigms are used to retrieve highly relevant information, then search precision is improved, but information diversity and exploratory capability deteriorate
Solution Approach 1:
The patent segments search results into multiple categories (e.g., popular articles, related topics, deep dives) rather than presenting a single linear list. This segmentation allows users to explore different types of content simultaneously, maintaining both precision (through relevant matching) and diversity (through categorical variety).
Solution Approach 2:
The patent adds a categorical dimension to traditional search results by organizing content into multiple classification layers. Instead of a one-dimensional relevance ranking, results are presented with multiple classification dimensions (popularity, relevance, depth, recency), enabling users to navigate content from different perspectives and discover diverse information.
2Ease of operation
If linear relevance-based search results are provided, then ease of operation is improved, but time consumption for exploratory searches increases
Solution Approach 1:
The system performs preliminary categorization and organization of search results before presentation. By pre-segmenting content into meaningful categories and pre-computing multiple classification dimensions, the system reduces the time users would otherwise spend manually exploring and filtering results, while maintaining ease of navigation through structured presentation.
3Ease of operation
If recommendation engines are used to suggest content based on popular selections, then ease of operation is improved, but content exploration capability deteriorates
Solution Approach 1:
Different sections of the search results present different qualities of content: popular articles section provides high-ease-of-operation content based on popularity, while related topics and deep dives sections provide high-exploration capability content with more diverse and niche topics. Each section serves a different user need locally rather than applying a single recommendation approach globally.
Data Source
AI summary
Systems and methods are disclosed for hierarchical categorical and sub-categorical topic modeling allowing, in response to a query in natural language, a set of results to be determined which are both semantically relevant to the user and diverse, by containing information complementary or adjacent to that of the query. Such a paradigm permits exploration and discovery of new topics and ideas in large collections of documents. In some embodiments, one or more non-negative matrix factorization (“NMF”) algorithms are applied in determining a hierarchical topic model including the semantically-related categories and sub-categories. The dataset may include authorized social media data collection, and machine learning techniques can optimize the generation of the topic model and/or the search results.