Contrastive Learning Model Category Tree Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current contrastive learning methods for query classification in e-commerce have relatively low classification accuracy due to their inability to effectively utilize the hierarchical information within category trees.
Innovation Solution
The method involves obtaining sample data with category information, predicting correlations between category information and query information using a contrastive learning model, establishing a loss function based on prediction accuracy, optimizing the loss function using semantic relationships between category information within the category tree, and training the model with the optimized loss function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current contrastive learning methods classify query information based on information from leaf categories within the category tree, then the classification process is simple, but the classification accuracy is relatively low
Solution Approach 1:
The loss function is segmented into multiple components: a first loss function for basic contrastive learning and a second loss function for hierarchical structure learning. This segmentation allows the system to maintain the simplicity of basic contrastive learning while adding hierarchical information processing to improve classification accuracy.
Solution Approach 2:
The patent introduces a hierarchical dimension to the loss function by incorporating parent-child category relationships. This adds a new dimension of structural information beyond simple leaf category comparisons, enabling the model to leverage the category tree hierarchy for improved accuracy without overly complicating the base contrastive learning framework.
2Measurement precision
If the loss function is optimized using semantic relationships between category information, then the classification accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent pre-processes the category tree to extract hierarchical relationships and semantic information before training. By preparing the structural information in advance, the model can utilize these pre-computed relationships during training without requiring excessive computational resources during the actual learning process.
Solution Approach 2:
The patent introduces an intermediary mechanism that processes hierarchical relationships separately from the main contrastive learning process. This intermediary layer handles the complex semantic relationship computations, allowing the core contrastive learning model to remain computationally efficient while still benefiting from hierarchical information.
Data Source
AI summary
A method for training a contrastive learning model, acquiring an optimization item, and pushing information includes: obtaining sample data including first category information and query information; predicting a correlation between the first category information and the query information using the contrastive learning model; establishing a loss function based on the prediction results, wherein the loss function is configured to characterize accuracy of the prediction results; optimizing the loss function based on a semantic relationship between the first category information and second category information, wherein the first category information and the second category information correspond to different semantic information in a category tree, and the semantic relationship is related to relative positions of the first category information and the second category information within the category tree; and training the contrastive learning model based on the optimized loss function.