Neural Network User Matching for Integration Process Visual Elements
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Solution Overview
Problem
Current methods for customizing data integration processes in information handling systems fail to accurately gauge user preferences for visual elements, leading to incomplete matching of users with similar needs, as they do not account for negative feedback and latent features influencing user decisions.
Innovation Solution
The integration process user matching system employs matrix factorization and deep learning via a neural network to generate user and visual element embedding matrices, capturing both positive and unrecorded negative feedback to identify optimal latent features and match users with similar preferences, thereby facilitating more accurate user matching and collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used to gauge user preferences, then the system is simple to operate, but the matching precision of users with similar needs is insufficient
Solution Approach 1:
The patent introduces an intermediary neural network model that acts as a mediator between user feedback data and preference measurements. This neural network processes raw interaction data, extracts latent features, and generates comprehensive user preference vectors, thereby improving measurement precision without requiring direct complex analysis of all user interactions.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based preference measurement methods with a neural network-based system. Instead of using simple counting or weighting mechanisms, the system employs deep learning models to automatically learn and infer user preferences from interaction patterns, significantly improving measurement accuracy.
2Measurement precision
If only positive feedback is considered, then the system is easier to implement, but the user preference measurement is incomplete
Solution Approach 1:
The patent implements a comprehensive feedback mechanism that processes both positive and negative user interactions. The neural network model analyzes various types of feedback signals, including explicit ratings, implicit engagement patterns, and disengagement behaviors, to create a balanced and complete understanding of user preferences.
Solution Approach 2:
The patent processes more feedback information than traditional systems would handle, including both positive and negative feedback, as well as latent features extracted from interaction patterns. This excessive processing of feedback data ensures completeness of preference measurement, with the neural network efficiently filtering and synthesizing the information.
3Measurement precision
If traditional matching methods are used, then the system is simpler, but the ability to identify latent features is insufficient
Solution Approach 1:
The patent replaces traditional rule-based or statistical matching methods with neural network-based latent feature extraction. The model automatically learns hidden patterns and features from user interaction data that are not immediately apparent, enabling more accurate identification of latent preferences and better user matching.
Solution Approach 2:
The patent transforms the matching problem by changing the parameters used for comparison. Instead of matching users based on explicit attributes alone, the system uses neural networks to generate latent feature vectors that capture underlying preferences and behaviors, enabling more nuanced and accurate matching.
Data Source
AI summary
A method of matching integration process users may comprise receiving execution logs recording data associated with selections of integration process visual elements, generating a user/visual element interaction matrix based on counts of integration process visual element selections, inputting the user/visual element interaction matrix into a trained neural network to generate an optimized user preference and visual element embedding matrices, determining a latent feature user preference value adapting positive user feedback to incorporate implicit negative feedback of users for the integration process visual elements based on the optimized user preference and visual element embedding matrices, identifying users associated with latent feature user preference values for one of the integration process visual elements falling within a preset range of each other as matching users, and transmitting identification of the matching users and identification of the one of the plurality of integration process visual elements to at least one matched user.