Semantic Category Classification via Parallel Learning
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Solution Overview
Problem
Current category classification systems face challenges in accurately and efficiently recommending categories for large-scale publication corpora, leading to suboptimal user experiences and increased gross merchandise volume due to high classification errors and slow response times.
Innovation Solution
The implementation of a parallel learning framework using sequence semantic embedding (SSE) and statistical language modeling (SLM) with gradient boosting machines (GBM) to derive labeled data from unsupervised user logs, enabling robust and fast category recommendations by encoding listing titles and category tree paths into semantic vector representations for similarity measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional category classification systems are used, then system complexity is reduced, but classification accuracy decreases and response time increases
Solution Approach 1:
The patent segments the category classification task into multiple independent learning models, each specializing in specific category relationships. This segmentation allows parallel processing of different category paths while maintaining high classification accuracy, resolving the contradiction between accuracy and system complexity
Solution Approach 2:
The patent implements selective category classification by identifying and processing only the most relevant category paths for each listing. This partial action approach reduces computational overhead and system complexity while maintaining high classification accuracy by focusing resources on critical classification decisions
2Speed
If traditional category classification systems are used, then system simplicity is maintained, but response time increases
Solution Approach 1:
The patent pre-computes category embeddings and stores them in an efficient retrieval structure before runtime. This preliminary action enables rapid response during actual classification by avoiding computationally intensive operations at query time, thus improving speed while managing system complexity
Solution Approach 2:
The patent replaces traditional mechanical classification algorithms with neural network-based semantic embedding models. This substitution enables parallel processing and faster computation, significantly improving response time while the modular architecture manages system complexity
3Measurement precision
If more classification models are deployed, then classification accuracy improves, but processing speed decreases
Solution Approach 1:
The patent segments the classification workload across multiple specialized models that process different category paths in parallel. This segmentation allows the system to deploy more models without sequential processing overhead, maintaining both high classification accuracy and processing speed through concurrent execution
Solution Approach 2:
The patent transforms the classification problem into a semantic embedding space where categories are represented as vectors. This dimensional transformation enables efficient similarity-based classification that scales with the number of models, allowing deployment of multiple models without linearly increasing processing time
Data Source
AI summary
In accordance with an example embodiment, large scale category classification based on sequence semantic embedding and parallel learning is described. In one example, one or more closest matches are identified by comparison between (i) a publication semantic vector that corresponds to at least part of the publication, the publication semantic vector based on a first machine-learned model that projects the at least part of the publication into a semantic vector space, and (ii) a plurality of category vectors corresponding to respective categories from a plurality of categories.