Messaging Connection Recommendations Using Privacy-Safe Hybrid Graphs
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Solution Overview
Problem
Generating meaningful connection recommendations for new users in a messaging system at registration time poses privacy concerns due to limited available signals, potentially leaking information about recommended profiles.
Innovation Solution
A connection recommendation methodology that obscures the connection source and distance by selecting recommendations from contact book matches and preserving the ratio of different categories of profiles, using a hybrid graph approach with one-hop, two-hop, and three-hop profiles to generate recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If connection recommendations are generated using available user signals at registration time, then the recommendations can be personalized and meaningful, but user privacy may be compromised due to potential leakage of information about recommended profiles
Solution Approach 1:
The patent introduces an intermediary processing layer that transforms direct connection signals into obscured recommendation data. Instead of directly exposing connection relationships, the system uses intermediate representations (such as aggregated statistics, distance metrics, or transformed features) that preserve recommendation quality while preventing privacy leakage. This intermediary mechanism allows the system to generate personalized recommendations without revealing sensitive connection information about recommended profiles.
2Measurement precision
If detailed connection information is used to generate recommendations, then recommendation accuracy improves, but privacy protection deteriorates
Solution Approach 1:
The patent segments connection information into multiple hierarchical levels or categories (e.g., direct connections, indirect connections, connection strength categories). Instead of using granular detailed connection data that would compromise privacy, the system aggregates connections into broader segments that maintain sufficient signal for accurate recommendations while reducing privacy risk. This segmentation allows the system to preserve recommendation accuracy by maintaining meaningful distinctions without exposing sensitive individual connection details.
3Ease of operation
If connection source and distance are disclosed in recommendations, then users can make informed connection decisions, but privacy-safe operation becomes difficult
Solution Approach 1:
The patent transforms sensitive connection parameters (such as exact connection distance, specific connection source identifiers) into modified parameter representations that preserve utility for user decision-making while enhancing privacy protection. This may involve converting exact distances into distance categories, masking specific source identifiers with aggregated or transformed values, or using differential privacy techniques to add controlled noise. The transformed parameters maintain enough information for users to make informed decisions about connection relevance while preventing inference of sensitive privacy information.
Data Source
AI summary
When a messaging system generates connection recommendations for a new user, who first registers with the messaging system, the signals available for generation of recommendations may be limited to the user's contact book matches. Using just this limited signal poses a concern associated with leaking information about users represented by the recommendations. The technical problem of generating connection recommendations for a user at registration time in a privacy-safe manner is addressed by a recommendation methodology that obscures the connection source and the connection distance of the recommended profiles.