Text Classification System Using Topic Frequency for New Item Recommendations
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Solution Overview
Problem
Online merchants face challenges in recommending recently released or rarely purchased items, as these lack sufficient sales statistics for effective co-purchase recommendations.
Innovation Solution
A method and system for programmatically classifying text by identifying topics, determining categories, and associating unique categories with text based on actual and expected category frequencies, surprise scores, and connected category scores, enabling accurate classification even with limited data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If co-purchase recommendations are based on sales statistics, then recommendation accuracy is improved, but recently released or rarely purchased items cannot be recommended due to insufficient data
Solution Approach 1:
The patent introduces text classification and topic modeling as intermediary techniques to bridge the gap between items with and without sales data. By classifying items based on their text descriptions, metadata, and topic associations, the system can generate recommendations for new or rare items through content-based filtering, while still utilizing sales statistics for well-established items. This intermediary approach allows the system to handle both scenarios effectively.
Solution Approach 2:
The patent changes the parameters used for recommendation from purely sales-based metrics to a hybrid approach that includes text-based classification scores, topic frequencies, and content similarity measures. This parameter change enables the system to evaluate and recommend items based on their textual characteristics rather than relying exclusively on historical sales data, thereby solving the problem of recommending new or rarely purchased items.
2Measurement precision
If text classification uses multiple categories and topics, then classification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the text classification process into distinct modules: topic identification, category assignment, frequency calculation, and scoring. Each module handles a specific aspect of classification independently, making the overall complex system more manageable and maintainable. The segmentation allows for targeted optimization of each component without redesigning the entire system.
Solution Approach 2:
The patent performs preliminary topic identification and category assignment before final classification scoring. By pre-processing the text to identify relevant topics and associate them with categories in advance, the system reduces the computational complexity of the final classification step. This preliminary action organizes the data structure to facilitate more efficient and accurate classification.
3Measurement precision
If category frequency analysis is performed for multiple topics, then classification precision is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by focusing category frequency analysis only on the most relevant topics identified for each text, rather than analyzing all possible topics. This selective approach maintains high classification precision by concentrating computational resources on the most significant topic-category relationships while reducing overall processing time by excluding less relevant analyses.
Data Source
AI summary
Systems and methods for programmatically classifying text are discussed herein. Some embodiments may provide for a system including circuitry configured to programmatically classify a block of text. For example, the circuitry may be configured to identify topics associated with the block of text and identify one or more categories for each of the topics. The circuitry may be further configured to determine unique categories across the one or more categories for each of the topics. For each unique category, an actual category frequency may be determined based on a number of times each of the topics in the block of text is associated with the unique category. The circuitry may be further configured to associate a unique category with the block of text based on the actual category frequency for each the unique category and one or more other actual category frequencies for one or more other unique categories.