Social Dating System Using Social Graph Filtering
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Solution Overview
Problem
Traditional social networking systems lack effective mechanisms for identifying and connecting users based on specific preferences or interest areas, such as relationship status or medical experiences, making it difficult for users to find suitable connections without extensive searching.
Innovation Solution
A system and method that utilize a social graph to identify candidate users matching a preference set, allowing users to request introductions through a graphical user interface, with the option to connect via connector members and facilitate communication, incorporating modules for filtering, permissions, and rewards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually search for connections in traditional social networking systems, then they can find some connections, but it requires extensive time and effort
Solution Approach 1:
The system automatically identifies and presents candidate connections to users based on their profile data and preferences without requiring manual searching. The social networking system performs self-service by autonomously querying member data, applying filtering criteria, and generating candidate lists, thereby eliminating the need for users to invest time in manual connection discovery
Solution Approach 2:
The system pre-computes and stores candidate connection lists based on member profiles, preferences, and filtering criteria before users request connections. By performing the identification and filtering operations in advance, the system prepares ready-to-present candidate lists that can be immediately displayed to users, significantly reducing their search time
2Adaptability or versatility
If the system identifies candidate users from the entire member database, then more potential connections are found, but privacy concerns increase
Solution Approach 1:
The system applies different levels of data accessibility to different members based on their privacy preferences. Some members have their profiles made publicly accessible for matching, while others have restricted access. The identification module queries member data selectively according to these localized privacy settings, ensuring that only members who have opted in are included in candidate lists, thus balancing matching capability with privacy protection
Solution Approach 2:
The system introduces an intermediary permission-checking mechanism between the candidate identification process and member data access. Before including a member in candidate lists, the system verifies their permission settings and only accesses their data if they have granted appropriate permissions. This intermediary layer protects member privacy while still enabling versatile connection matching among consenting members
3Measurement precision
If the system provides detailed filtering options for candidate selection, then connection precision improves, but system complexity increases
Solution Approach 1:
The filtering system is segmented into multiple independent, modular components that can be applied sequentially. Each filter (e.g., geographic location, relationship status, age range, gender) operates as a separate module that processes candidate lists independently. This segmentation allows the system to provide detailed filtering options without creating a monolithic complex system, as each filter can be developed, maintained, and adjusted independently
Solution Approach 2:
The filtering system is designed to be dynamic and configurable, allowing users to enable or disable specific filter criteria based on their needs. The identification module can adaptively apply different combinations of filters depending on user preferences and connection types. This dynamic nature enables high precision matching while keeping the system flexible and manageable, avoiding rigid complexity
Data Source
AI summary
Systems and methods for social dating are provided. In particular, some embodiments provide recommendations for connections (i.e., candidate users) based on a user's social graph. These recommendations can identify potential single individuals that may be good matches for dating or can identify individuals with other commonalities or shared experiences to create dialog. For example, the recommendations for a user can include single individuals that are friends of a friend. The user can be presented with information about the recommendation and then ask for an introduction from the user's friend. As another example, the recommendations can be based on subject matter selected by the user (e.g., twins, cancer, phone type, etc.) with or without any friendship connections. The user may also be able to filter or further refine the searches based on other criteria such as interests, location, age, and/or other constraints. Rewards can be provided in some cases to encourage participation.