NLP Vector Mapping for Seller Industry Prediction
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Solution Overview
Problem
Traditional analysis techniques are insufficient for identifying potential security and credit risks in users, especially when user data is absent, unreliable, or outdated, and sudden changes in a seller's industry can indicate fraudulent activity or credit risks.
Innovation Solution
Applying natural language processing (NLP) techniques, such as word2vec, to model user data by analyzing buying patterns of customers, mapping sellers into vector spaces based on their buying sequences, and using k-nearest neighbors to predict the industry of sellers with insufficient textual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional analysis techniques are used to identify user risks, then the analysis process is simple, but the ability to detect security and credit risks is insufficient
Solution Approach 1:
The patent replaces traditional mechanical analysis techniques with natural language processing (NLP) algorithms. Specifically, it uses word2vec to convert user characteristics into vector representations, enabling the system to capture semantic relationships and patterns that traditional methods miss, thereby improving risk detection accuracy while managing complexity through automated processing.
Solution Approach 2:
The patent transforms user characteristics from categorical text data into continuous vector representations. By changing the parameter representation from discrete text fields to continuous vector spaces, the system enables more nuanced analysis of user profiles, behavior patterns, and risk factors, improving the reliability of risk assessment.
2Reliability
If user characteristic data is collected and analyzed, then risk identification capability improves, but the complexity of data processing increases
Solution Approach 1:
The patent replaces complex manual data processing with NLP-based automated processing. The word2vec model automatically converts unstructured user characteristic data into structured vector representations, and machine learning algorithms automatically analyze these vectors to identify risks, reducing processing complexity while improving reliability.
Solution Approach 2:
The patent creates vector representations as copies of user characteristic data. Instead of directly analyzing complex text data, the system creates simplified vector copies that capture essential information, making the data easier to process and analyze while preserving the key features needed for risk identification.
3Measurement precision
If NLP techniques are applied to model user data, then prediction accuracy improves, but computational resources required increase
Solution Approach 1:
The patent performs preliminary processing by pre-training word2vec models on large corpora of user data. This preliminary action creates ready-to-use vector representations and embeddings that can be quickly applied to new users without requiring intensive real-time computation, thereby improving prediction accuracy while reducing ongoing computational resource consumption.
Data Source
AI summary
Methods and systems for identifying characteristics of a user (e.g., a seller) based on Natural Language Processing (NLP). Transaction data of buyers may be collected to generate a sequence paragraph of seller name information for each buyer. NLP techniques such as word2vec may be used to vectorize the seller name information to determine relationships between sellers. Industry information may be determined using the vectors. Reliability checks may be performed to determine whether the data is robust to label the determined data.