Topic Modeling for User Interaction Data Dimensionality Reduction
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Solution Overview
Problem
Existing systems lack the ability to effectively extract and analyze personal information from user interactions across various mediums, such as textual chats and voice interactions, to tailor services and products efficiently.
Innovation Solution
The use of text mining and topic modeling to automatically extract personal information from user interactions, reducing data dimensions and enabling faster computations, allowing for a lower-dimensional representation of the data and improved analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text mining and topic modeling are used to extract personal information from user interactions, then information extraction capability is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex text analysis task into distinct stages: initial text processing, topic modeling to identify key themes, and journey of life attribute extraction. This segmentation allows each component to be optimized independently, reducing overall computational complexity while maintaining extraction precision.
Solution Approach 2:
The patent introduces topic models as an intermediary layer between raw user interaction text and final personal information extraction. This intermediary structure organizes unstructured text into meaningful topics, making subsequent attribute extraction more efficient and less computationally intensive.
2Productivity
If dimensional reduction is applied to represent text data, then computational speed is improved, but information representation completeness may be reduced
Solution Approach 1:
The patent transforms high-dimensional text data into a lower-dimensional topic space while preserving essential semantic relationships. By mapping text to topic distributions, the system achieves dimensional reduction that maintains information completeness through the probabilistic nature of topic modeling.
Solution Approach 2:
The patent changes the representation parameters of text data from raw word frequencies to topic probabilities. This parameter transformation enables compact representation that retains essential information while reducing dimensionality, balancing computational efficiency with information preservation.
Data Source
AI summary
Embodiments of the invention relate to managing user interactions and, more particularly, to performing analysis on data generated by user interactions. Embodiments of the invention use text mining to extract personal information of users from user interactions automatically. A topic model is used to reduce the number of dimensions required to represent the text, yet all the information of interest is highly pronounced. This enables a lower dimensional representation of the data leading to significantly faster computations.