AI Recommendation Split Between Device and Server for User Privacy
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Solution Overview
Problem
Existing recommendation systems face challenges in balancing user privacy protection and resource efficiency, particularly when deployed on user devices, as they may expose personal information and are limited by device resources.
Innovation Solution
A decentralized approach where an electronic device processes user responses and interacts with a server to utilize an AI-based recommendation model, encoding user interactions and item information to provide personalized recommendations efficiently and securely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the recommendation system is constructed in the server, then user information security is improved, but system cost increases
Solution Approach 1:
The recommendation system is segmented into two parts: the recommendation model resides on the server while the recommendation application runs on the user device. This segmentation allows user information processing to occur locally on the device, improving security while reducing the complexity and cost of server-side infrastructure requirements.
2Device complexity
If the recommendation system is constructed in the user device, then system cost is reduced, but user information security deteriorates
Solution Approach 1:
The recommendation model is extracted from the user device and placed on the server. This extraction allows the device to run a lighter application that processes data locally, reducing device resource requirements and cost while maintaining user information security through local processing.
3Reliability
If the recommendation system is constructed in the user device, then user information security is improved, but system performance is limited
Solution Approach 1:
Different components are placed in different locations with appropriate qualities: the recommendation model (requiring high computational power) is placed on the server, while the recommendation application (requiring user interaction and local data access) is placed on the user device. This distribution optimizes both security and performance.
Data Source
AI summary
An electronic device is provided. The electronic device includes a communication circuit for communicating with a server, a display, at least one processor connected, and a memory, wherein the memory stores instructions causing, when executed, the at least one processor to collect an intermediate operation value corresponding to an item to which a user responds among first items introduced to the user through the display, store the collected intermediate operation value, receive item information about second items and intermediate operation values, input, as an input value, the intermediate operation values received from the server, input first user information indicating a profile of the user and the user response item vector, obtain scores assigned to each of the second items from a result value output from the recommendation model, and provide the item information to the user through the display on the basis of the scores.