Non-textual Topic Modeling via Collaborative Filtering
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Solution Overview
Problem
Conventional topic modeling techniques are language-specific and text-based, limiting their applicability to specific languages, whereas real-world applications require language-agnostic methods to analyze user interactions and identify important item dimensions across diverse domains.
Innovation Solution
The proposed solution employs collaborative filtering to generate ranked lists of similar items based on user behavior, which are then used as input for topic modeling algorithms to determine overlapping topics without relying on textual data, allowing for language-agnostic analysis of item dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional topic modeling techniques are used, then language-specific text analysis is achieved, but language-agnostic applicability is lost
Solution Approach 1:
The patent replaces text-based input processing with collaborative filtering-based similarity computation. Instead of processing textual data through language-specific topic models, the system uses user interaction data to generate item similarity matrices, which are then processed through topic modeling algorithms to produce language-agnostic topic representations.
Solution Approach 2:
The patent changes the input parameters from text-based features to collaborative filtering-based similarity scores. By transforming the input from linguistic data to numerical similarity matrices derived from user behavior, the system enables topic modeling to operate independently of language while preserving the ability to identify meaningful item dimensions.
2Adaptability or versatility
If text-based topic modeling is used, then semantic feature detection is achieved, but applicability to non-text domains is limited
Solution Approach 1:
The patent creates a universal topic modeling framework that can process multiple types of data through collaborative filtering. By using user interaction data as a common foundation for generating similarity matrices, the system can apply topic modeling uniformly across diverse domains including e-commerce, entertainment, and information services, regardless of whether the underlying data is textual or not.
3Adaptability or versatility
If collaborative filtering is used to generate item similarities, then language-agnostic item dimension identification is achieved, but text-based semantic analysis is lost
Solution Approach 1:
The patent introduces collaborative filtering-based item similarity matrices as an intermediary representation between user interaction data and topic modeling results. This intermediary structure preserves the language-agnostic nature of the input while providing a structured format that topic modeling algorithms can process effectively, thereby maintaining measurement precision without requiring textual input.
Data Source
AI summary
Devices and techniques are generally described for non-textual topic modeling. In some examples, a first item of a plurality of items may be identified. A first ranked list of items from the plurality of items may be generated for the first item using collaborative filtering. Topic modeling input data representing associations between the first item and each item in the first ranked list may be generated. Second data may be generated by inputting the topic modeling input data into a topic modeling algorithm. The second data may comprise one or more topics for the first ranked list of items.