Privacy Control Server Using Segmented Models for Cold Start
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in managing privacy control due to the 'cold start' problem for initial subscribers and model update issues, where existing systems struggle to accurately recommend privacy settings without a history of personal information provision, leading to inefficiencies and inaccuracies in recommendation services.
Innovation Solution
A privacy control server and method that utilize common privacy control recommendation models learned from multiple users' histories, combined with user preference determination through question items and reinforcement learning, to provide personalized privacy control recommendations, even for initial subscribers, and update models in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a recommendation model uses machine-learning technique to classify responses based on frequency of data, then the recommendation accuracy is improved, but the history of each user is not reflected in the recommendation model until the frequency of the user's history exceeds a predetermined frequency
Solution Approach 1:
The patent segments the recommendation model into two distinct components: a global recommendation model that handles general privacy control recommendations based on aggregated user data, and a user-specific recommendation model that captures individual user history and preferences. This segmentation allows the system to leverage both global patterns and individual user behavior without requiring the user-specific model to reach a minimum frequency threshold, thereby resolving the contradiction between recommendation accuracy and user history reflection.
Solution Approach 2:
The patent implements preliminary action by pre-training the global recommendation model with aggregated privacy control data from multiple users before any user-specific history is accumulated. This preliminary global model serves as a baseline that can immediately provide recommendations to new users, while user-specific models are continuously refined as individual history accumulates, eliminating the need to wait for frequency thresholds to be met.
2Measurement precision
If the entire model is updated to reflect actual history of each user, then the recommendation accuracy is improved, but a lot of time and expense are consumed
Solution Approach 1:
The patent divides the recommendation model into a global component and user-specific components, allowing updates to be performed at different scopes. The global model can be updated periodically with aggregated data, while user-specific models are updated individually as new user history becomes available. This segmentation enables targeted updates that improve accuracy without requiring complete model retraining, thereby reducing time and computational expense.
Solution Approach 2:
The patent implements dynamics by making the recommendation system adaptive and flexible in its update strategy. The system dynamically adjusts which models to update and when, based on the amount of available user history and the complexity of the data. This dynamic approach allows the system to balance between maintaining a stable global model and adapting to individual user patterns, optimizing the trade-off between accuracy and update time.
3Measurement precision
If a cold start problem is addressed by using user preference detection based on accumulated history, then the recommendation accuracy is improved, but it is not easy to recommend privacy control to initial subscribers
Solution Approach 1:
The patent applies preliminary action by pre-training the global recommendation model with aggregated privacy control preferences from the general user population before any individual user accumulates history. This pre-trained global model serves as an immediate resource for initial subscribers, enabling the system to provide personalized recommendations from the first interaction without requiring the user to have any prior history, thus resolving the cold start problem while maintaining recommendation accuracy.
4Device complexity
If the user interface of mobile terminal is limited, then the device complexity is reduced, but the user may make a mistake in determining privacy control
Solution Approach 1:
The patent implements self-service by having the system automatically generate and present privacy control recommendations based on the user's context, device type, and accumulated history. The system performs the complex analysis and determination of appropriate privacy settings autonomously, presenting the user with simplified options or automatic decisions. This shifts the burden from the user to make informed privacy determinations to the system to perform the analysis, thereby maintaining simple interfaces while improving determination accuracy and reliability.
Data Source
AI summary
Disclosed herein are an apparatus and method for recommending privacy control. A privacy control server includes multiple common privacy control recommendation models learned based on personal information provision histories of multiple users, a user preference determination unit for generating at least one question item based on the multiple common privacy control recommendation models and determining a privacy control preference of a user based on a result of a user answer to the at least one question item, and a common privacy control recommendation model provision unit for providing a common privacy control recommendation model selected to match the privacy control preference of the user to the user.