Digital Magazine Topic Model Using Concept Distributions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge lies in efficiently identifying and recommending content items to users in digital magazines, as existing methods struggle with correlating topics with content items, especially when dealing with a large volume of diverse content, and are often computationally intensive.
Innovation Solution
A digital magazine server analyzes content items by associating topics based on characteristics such as titles and descriptions, using Dirichlet distributions and a bag-of-words model to determine concept and topic distributions, and iteratively modifies these distributions to generate a model that learns dependencies and correlations between topics and concepts, allowing for efficient topic assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to correlate topics with content items, then topic discrimination is improved, but computational complexity increases significantly
Solution Approach 1:
The patent transforms the topic correlation problem from a direct high-complexity computation into a two-stage process: first generating concept distributions from content characteristics (lower complexity), then deriving topic correlations from these concept distributions (lower complexity). This parameter transformation resolves the contradiction by changing the computational approach while maintaining topic discrimination accuracy.
2Adaptability or versatility
If conventional topic correlation methods are applied to large volumes of diverse content, then topic coverage is improved, but identification efficiency deteriorates
Solution Approach 1:
The patent segments the topic identification process into distinct stages: content characteristic extraction, concept distribution generation, and topic correlation derivation. This segmentation allows each stage to operate independently and efficiently on large volumes of diverse content, improving both topic coverage and identification efficiency simultaneously.
3Measurement precision
If more content items are analyzed for recommendation, then recommendation quality is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-computing concept distributions for content items based on their characteristics. These pre-computed concept distributions are then reused for multiple topic correlation analyses and recommendation queries, significantly reducing processing time while maintaining recommendation quality when analyzing large numbers of content items.
Data Source
AI summary
A digital magazine server generates a model to associate topics with content items. To generate the model, the digital magazine server selects a set of content items that have been included in one or more digital magazines. For each content item of the set, the digital magazine server determines a distribution of concepts associated with a content item of the set based on characteristics of digital magazines including the content item of the set and a distribution of topics associated with the content item of the set based on words included in the content item. Additionally, the digital magazine server determines a parameter defining a relationship between the distributions of concepts and the distributions of topics associated with content items of the set. A model based on the distributions of concepts and of topics as well as the parameter is generated and stored for application to content items.