Event Categorization via TF-IDF Feature Analysis
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Solution Overview
Problem
Online event management systems face challenges in efficiently categorizing events, as manual categorization is time-consuming and expensive, and existing automatic systems often misclassify events due to improper weight assignment of textual analysis.
Innovation Solution
An event management system uses a feature-based categorization model that generates sets of features through term-frequency-inverse-document-frequency (tf-idf) analysis and supervised learning to accurately categorize events by comparing event listings with predefined categories, reducing manual intervention and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual categorization is used to ensure accurate event classification, then categorization precision is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces the manual mechanical categorization process with an automated computer-based system that uses supervised learning algorithms and tf-idf analysis to classify events, thereby eliminating time-consuming manual intervention while maintaining high categorization precision through machine learning models trained on labeled event data
Solution Approach 2:
The system enables self-service categorization by automatically analyzing event listings and assigning categories without human intervention, using trained classification models that independently process and categorize events based on their textual content and features
2Productivity
If existing automatic categorization systems are used to reduce manual effort, then productivity is improved, but categorization precision deteriorates due to improper weight assignment
Solution Approach 1:
The patent changes the parameter weighting approach by using tf-idf (term frequency-inverse document frequency) analysis to dynamically determine feature weights based on their discriminative power across the event corpus, replacing improper fixed weight assignments with data-driven adaptive weighting that improves categorization precision while maintaining automation
Solution Approach 2:
The system implements feedback mechanisms through supervised learning where the model is trained on labeled event data, continuously improving its categorization accuracy by learning from correct classifications and adjusting its feature weight assignments based on performance feedback from training examples
3Measurement precision
If manual categorization of tens of thousands of events is performed to ensure accurate classification, then categorization precision is improved, but productivity decreases due to the large volume of work
Solution Approach 1:
The patent replaces manual categorization mechanics with automated computer-based processing that can handle tens of thousands of events simultaneously, using algorithms that rapidly analyze event listings and assign categories without the time constraints of human workers, thereby maintaining precision while dramatically increasing productivity
Data Source
AI summary
In one embodiment, a method includes receiving event information associated with an event listing, the event information being inputted by a user of an online event management system at a client device; transmitting the event information to the online event management system, the event listing being synchronously categorized with the sending of the event information; and receiving from the online event management system one or more categories associated with the event listing for display to the user at the client device, the categories being automatically determined based on categorization models of the online event management system in response to the transmitted event information.