Client-Server Segmentation for Privacy-Preserving User Behavior Prediction
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Solution Overview
Problem
Existing user behavior prediction methods in e-commerce platforms face challenges in balancing accurate item recommendations with the need to protect user privacy, particularly in federated learning settings where implementation complexity is high.
Innovation Solution
The proposed solution involves decomposing the item recommendation module into a user behavior embedding engine deployed on the client and an item embedding engine on the server. This approach allows for local processing of user data to protect privacy while leveraging server resources for item recommendation processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to protect user privacy by processing data locally, then user privacy is protected, but implementation complexity at the client increases significantly
Solution Approach 1:
The recommendation system is segmented into two distinct components: a user behavior embedding engine deployed on the client device and an item embedding engine deployed on the server. This segmentation allows privacy-sensitive user data processing to occur locally while item recommendation logic resides on the server, reducing client implementation complexity while maintaining privacy protection.
Solution Approach 2:
The user behavior embedding engine acts as an intermediary component that processes user data locally and generates behavior embeddings, which are then transmitted to the server for item recommendation. This intermediary approach enables federated learning functionality while simplifying the overall system architecture and reducing client-side complexity.
2Reliability
If all user data processing is performed locally to protect privacy, then user privacy is protected, but computational overhead and resource consumption increase
Solution Approach 1:
The computational workload is segmented between client and server: the user behavior embedding engine on the client performs only local user data processing and generates compact behavior embeddings, while the item embedding engine on the server handles item processing and recommendation generation. This segmentation reduces client computational overhead while maintaining privacy protection.
Solution Approach 2:
Instead of performing complete recommendation processing locally, the system performs only the necessary partial action of generating user behavior embeddings on the client, while delegating item processing and final recommendation to the server. This partial local processing approach minimizes client computational overhead while preserving privacy.
3Measurement precision
If a centralized recommendation system processes all user data on the server, then recommendation accuracy can be improved, but user privacy protection is compromised
Solution Approach 1:
The recommendation system is segmented into client-side user behavior processing and server-side item recommendation processing. This segmentation enables the system to maintain recommendation accuracy through coordinated processing while protecting user privacy by keeping sensitive user data processing local to the client device.
Solution Approach 2:
The user behavior embedding engine serves as an intermediary that transforms user data into behavior embeddings locally, which are then transmitted to the server for item recommendation. This intermediary mechanism enables accurate recommendations while protecting user privacy by preventing raw user data from leaving the client device.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, a device, and a computer program product for user behavior prediction. In some embodiments, at a client, a first user behavior embedding engine in the client generates behavior prediction information of a target user based on feature information of the target user. The client sends the behavior prediction information of the target user to a server, and receives information about a target item recommended for the target user from the server. Such method enables user privacy-related information to be processed only locally, thereby not only ensuring user privacy and security, but also significantly reducing overall resource overhead.