Reward Model Generation for Targeted User Information Search
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Solution Overview
Problem
Existing online networks face challenges in efficiently locating and removing user information due to the complexity of information dissemination across various interconnected platforms, with issues such as data exposure and difficulty in scrubbing user data from networks.
Innovation Solution
A computer-implemented method and system that generates an online outreach-based reward model to identify and search for user information by determining key features of a user's online presence, deriving a reward model to indicate likely locations for information, and using machine-learning to enhance search efficiency and focus on relevant areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive search across all online networks is performed to locate user information, then the completeness of information retrieval is improved, but the time and computational resources required increase significantly
Solution Approach 1:
The system performs preliminary analysis of user features and online outreach patterns before executing the search. By pre-determining which online networks and locations are most likely to contain the user information based on feature analysis, the system avoids searching all networks comprehensively, thus reducing search time while maintaining retrieval completeness
Solution Approach 2:
The reward model assigns different weights and priorities to different online networks and locations based on their likelihood of containing user information. Instead of uniform searching, the system concentrates search efforts on high-probability locations identified through feature analysis, optimizing the distribution of search resources across different networks
2Reliability
If the search covers all possible online networks and locations, then the reliability of finding user information is improved, but the complexity of the search system increases
Solution Approach 1:
The reward model acts as an intermediary between the feature analysis and the actual search execution. It translates user features and online outreach data into prioritized search locations, simplifying the search system by providing a clear decision-making layer that determines where to search without requiring complex coordination across all networks
Solution Approach 2:
The search system is segmented into distinct functional components: feature identification, online outreach determination, reward model generation, and search execution. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by breaking down the complex task of multi-network searching into manageable stages
3Ease of operation
If generic search parameters are used across all users, then the ease of operation is improved, but the effectiveness of locating specific user information decreases
Solution Approach 1:
The search parameters are made dynamic and adaptive rather than static and generic. The reward model continuously adjusts search priorities based on user-specific features and online outreach patterns, allowing the system to automatically adapt to each user's unique information distribution patterns without requiring manual configuration
Solution Approach 2:
The system performs self-configuration by automatically generating personalized search strategies based on analyzed user features. The reward model autonomously determines optimal search locations and parameters for each user without external intervention, making the system both easy to operate and highly effective for specific user information retrieval
Data Source
AI summary
Online outreach based reward model generation is described. A set of features that are indicative of an online outreach for a user are determined, the online outreach originating from a particular online network. Based on this set of features, an online outreach for the user originating from the particular online network is determined. A reward model is derived from the online outreach for the user. The reward model indicates locations within the particular online network that are to be searched for user information.