Topic Mapping Index for Social Network Content Categorization
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Solution Overview
Problem
Current social networking systems lack an effective method to classify and categorize text content into broad topics, which limits their ability to provide personalized recommendations and rankings based on user interests.
Innovation Solution
A topic tagging system that uses a topic-mapping index to map input topics to broader categories with associated similarity scores, allowing for the identification of overarching topics in text content, such as 'baseball' for a post about 'Babe Ruth' and 'New York Yankees', and aggregates these scores to rank content for user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a topic tagging system is implemented to classify text content into broad topics, then the ability to provide personalized recommendations and rankings is improved, but the system complexity increases
Solution Approach 1:
The patent segments the topic classification process into multiple independent components: a topic tagger that extracts input topics from text, a topic-mapping index that stores pre-computed mappings between input topics and broad topics with similarity scores, and an aggregation module that combines scores. This segmentation allows each component to be developed, maintained, and optimized independently while working together to provide personalized recommendations.
Solution Approach 2:
The patent implements preliminary action by pre-computing and storing the topic-mapping index in advance. The index contains pre-established mappings between various input topics and broad topics along with their similarity scores. This pre-computation eliminates the need for complex real-time calculations during recommendation generation, reducing system complexity while maintaining adaptability for personalized recommendations.
2Measurement precision
If multiple input topics are mapped to broad topics with similarity scores and aggregated, then the accuracy of content categorization is improved, but the computational time increases
Solution Approach 1:
The patent applies preliminary action by pre-computing the topic-mapping index that contains all possible mappings between input topics and broad topics with their similarity scores. This pre-computation is performed offline and stored for rapid retrieval during runtime. When classifying content, the system simply queries this pre-built index and aggregates scores, avoiding complex real-time computations while maintaining high categorization accuracy through multiple topic mappings.
Solution Approach 2:
The patent uses copying by creating a replicated topic-mapping index that can be quickly queried multiple times. Instead of recomputing mappings for each content item, the system copies and retrieves relevant mappings from the pre-built index, significantly reducing computational time while maintaining accurate categorization through the aggregated similarity scores from multiple input topics.
3Speed
If a topic-mapping index with pre-calculated similarity scores is used, then the speed of topic identification is improved, but the memory requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for topic identification into the topic-mapping index: the mappings between input topics and broad topics along with their similarity scores. Rather than storing complete topic descriptions, metadata, or unrelated data, the system extracts and stores only the critical mapping relationships and numerical scores, reducing memory requirements while maintaining fast topic identification speed through efficient index queries.
Solution Approach 2:
The patent applies local quality by optimizing the topic-mapping index structure to store data in a format that balances memory efficiency with retrieval speed. The index stores compact representations of topic mappings with similarity scores, using data structures that minimize memory footprint while enabling rapid lookup. This localized optimization of the index structure allows fast topic identification without excessive memory consumption.
Data Source
AI summary
In one embodiment, a method includes deriving input topics based on a content item, generating a matrix of scores for the input topics according to a first set of cross-indexed topics, where each of the scores indicates a degree of similarity between a corresponding one of the input topics and a corresponding one of the first cross-indexed topics, calculating a total score for each of the first cross-indexed topics based on the scores for the first cross-indexed topic across all of the input topics, and selecting one or more of the first cross-indexed topics based on the total scores of the first cross-indexed topics. Deriving the input topics may include using a topic tagger to identify the topics based on the content item. The first set of cross-indexed topics may be generated from a database of topics, such as an online encyclopedia.