Dynamic Correlated Topic Model for Temporal Evolution
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Solution Overview
Problem
Correlated topic models (CTMs) fail to dynamically model the evolution of topic popularity, representation, and co-occurrence over time, limiting their ability to capture changes in the meaning and correlation of topics as they evolve.
Innovation Solution
A dynamic correlated topic model (DCTM) is introduced, which uses Gaussian processes and a generalized Wishart process to jointly model the evolution of topic popularity, representation, and correlations over time, incorporating temporal dynamics and enabling mini-batch training through stochastic variational inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional correlated topic models (CTM) are used, then topic correlation can be modeled, but dynamic evolution of topic popularity, representation, and correlation over time cannot be captured
Solution Approach 1:
The patent applies dynamics by transforming the static CTM model into a dynamic one where topic parameters (popularity, representation, and correlation) are explicitly time-dependent. The model uses time-varying parameters that evolve continuously over time, allowing it to capture the dynamic evolution of topics while maintaining a structured probabilistic framework.
Solution Approach 2:
The patent embeds the traditional CTM structure within a dynamic framework by nesting time-dependent Gaussian process models within the topic model. The dynamic correlated topic model contains the static CTM as a core component while adding temporal layers, creating a nested structure where the dynamic model preserves the static model's functionality while extending it with temporal capabilities.
2Measurement precision
If dynamic modeling of topic evolution is implemented, then temporal changes can be captured, but computational scalability to large datasets is reduced
Solution Approach 1:
The patent segments the large dataset into mini-batches for training, allowing the dynamic model to be trained incrementally rather than requiring all data to be processed simultaneously. This segmentation enables the model to scale to large datasets by processing data in manageable chunks while maintaining temporal modeling accuracy through the use of stochastic variational inference.
Solution Approach 2:
The patent replaces traditional maximum likelihood estimation with stochastic variational inference, substituting a computationally intensive mechanical optimization process with a more scalable probabilistic approach. This substitution allows the dynamic model to handle large datasets efficiently by using randomized gradient estimates and variational approximations instead of exact computations.
Data Source
AI summary
A system implements a dynamic correlated topic model (DCTM) to model an evolution of topic popularity, topic representation, and topic correlation within a set of documents, or other dataset, that spans a period of time. For example, the DCTM receives the set of documents and a quantity of topics for modeling. The DCTM processes the set by analyzing words of the documents, identifying word clusters representing the topics, and computing, for each topic, various distributions using continuous processes to capture a popularity, representation, and correlation with other topics across the period of time. In other examples, the dataset are user listening sessions comprised of media content items. Media content metadata (e.g., artist or genre) of the media content items, similar to words of a document, can be analyzed and clustered to represent topics for modeling by the DCTM.