Geolocation-Based Social Graph Recommendations
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Solution Overview
Problem
Users of social networking systems lack the ability to receive relevant and timely information based on their interests, connections, and location, and third-party content providers struggle to effectively utilize these systems to reach potential customers at the right time and place.
Innovation Solution
A computing device provides personalized recommendations to users by cross-referencing their geographic location with a social graph, suggesting content, applications, or actions based on physical proximity and social connections, such as nearby friends or local interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If social networking systems provide general information to all users, then information coverage is broad, but information relevance to individual users and their locations is poor
Solution Approach 1:
The patent applies local quality by providing different information content to different users based on their specific location and social connections. Instead of uniform information distribution, the system tailors recommendations to local contexts - users receive information relevant to their geographic area and social circle, making the information quality locally optimized rather than globally uniform.
Solution Approach 2:
The system performs preliminary action by pre-processing and organizing social graph data and location information before users need it. The system maintains pre-computed social graphs and location databases, so when a user requests information, the system can quickly retrieve and filter relevant data without performing complex computations in real-time, thus reducing perceived system complexity.
2Loss of information
If the system provides personalized recommendations based on social graph and location data, then information relevance improves, but data processing complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary action by pre-computing and maintaining social graphs that capture relationships between users, locations, and interests. This preprocessing allows the system to quickly generate personalized recommendations by simply querying the pre-structured data, rather than performing complex graph analyses in real-time when users request information, thus improving timeliness while managing processing complexity.
Solution Approach 2:
The patent applies copying by creating simplified representations or indexes of the complex social graph data. Instead of processing the entire social graph for each recommendation query, the system maintains copied or indexed versions of the data that enable fast retrieval and filtering, reducing computational requirements while maintaining recommendation quality.
3Loss of information
If the system limits suggestions based on physical distance and social connections, then suggestion relevance increases, but the quantity of available suggestions decreases
Solution Approach 1:
The system applies local quality by focusing suggestions on locally relevant content within specific geographic radii and social connection depths. Rather than providing a large number of generic suggestions, the system concentrates on high-quality, location-aware recommendations that are relevant to the user's immediate context, accepting fewer suggestions in exchange for higher local relevance.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the radius and degree of separation parameters based on user preferences, context, and behavior. The system can expand or contract the search parameters to balance between suggestion quantity and quality, allowing flexible control over how far to extend geographic and social connection limits based on specific situations.
Data Source
AI summary
In one embodiment, a method includes accessing geolocation data indicating a first geolocation of a mobile computing device of a user of a social-networking system. The social-networking system including a graph that includes a number of nodes and edges connecting the nodes. A first node in the graph corresponds to the user. The method also includes identifying one or more second nodes in the graph connected to the first node. Each of the second nodes being associated with a second geolocation. Each of the second nodes being connected to the first node within a pre-determined threshold number of degrees of separation with at least one edge corresponding to the activity socially relevant to the user. Each edge in the graph represents a single degree of separation within the graph. The identifying being based on a determination that the second geolocation is within a pre-determined threshold distance of the first geolocation.