Interpretable User Modeling via Intent Neural Network
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Solution Overview
Problem
Conventional user modeling approaches struggle with interpreting unstructured user behavior data, particularly due to the large semantic gap between unstructured data and human-readable language, and the failure to account for temporal context, leading to difficulties in understanding user behavior effectively.
Innovation Solution
An interpretable user modeling system utilizing a recurrent neural network, specifically an intent neural network, is developed to bridge the semantic gap by leveraging auxiliary text-based data, incorporating a semantics memory unit for sequence-to-sequence learning, and using tutorial data to provide human-readable interpretations of user log data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional user modeling methods are used on unstructured user data, then the modeling process can be performed, but the interpretation of user behavior becomes difficult due to the large semantic gap between unstructured data and human readable language
Solution Approach 1:
The patent introduces an intermediary translation layer that converts unstructured user data into structured representations with human-readable explanations. This intermediary system includes a parser that extracts semantic meaning from unstructured data and generates natural language descriptions, effectively bridging the semantic gap without requiring direct interpretation of raw unstructured data.
Solution Approach 2:
The patent replaces traditional mechanical text processing methods with neural network-based semantic analysis. Instead of using rule-based systems or keyword matching to interpret unstructured data, the system employs deep learning models that automatically understand and translate unstructured user data into meaningful structured representations with human-readable explanations.
2Loss of information
If conventional user modeling methods are used, then processing can be completed, but temporal context in unstructured user data is not accounted for, causing important aspects of user behavior to be lost
Solution Approach 1:
The patent applies preliminary temporal analysis by embedding time-aware features into the user data representation before processing. The system pre-processes unstructured user data to extract and incorporate temporal patterns, such as sequence of actions and timing information, into the structured representation, ensuring that temporal context is preserved throughout the modeling process.
Solution Approach 2:
The patent implements dynamic temporal modeling by using recurrent neural networks or transformer-based sequences that capture evolving user behavior patterns over time. The system dynamically adjusts its interpretation of user data based on temporal sequences, allowing the model to understand how user behavior changes and evolves, thereby preserving important temporal aspects of user behavior.
3Adaptability or versatility
If unstructured user data is used for user modeling, then more comprehensive user behavior data can be captured, but the data cannot be easily interpreted to understand user behavior
Solution Approach 1:
The patent introduces an intermediary translation layer that converts unstructured user data into structured representations with human-readable explanations. This intermediary system includes a parser that extracts semantic meaning from unstructured data and generates natural language descriptions, effectively bridging the semantic gap without requiring direct interpretation of raw unstructured data.
Solution Approach 2:
The patent transforms unstructured user data by changing its parameter representation from raw unstructured format to structured format with explicit semantic parameters. The system applies parameter transformation techniques that convert unstructured text, logs, or other data into structured representations with defined attributes, making the data both comprehensive and interpretable simultaneously.
Data Source
AI summary
Methods and systems are provided for generating interpretable user modeling system. The interpretable user modeling system can use an intent neural network to implement one or more tasks. The intent neural network can bridge a semantic gap between log data and human language by leveraging tutorial data to understand user logs in a semantically meaningful way. A memory unit of the intent neural network can capture information from the tutorial data. Such a memory unit can be queried to identify human readable sentences related to actions received by the intent neural network. The human readable sentences can be used to interpret the user log data in a semantically meaningful way.