Social Graph Location Ranking via Engagement Scoring
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Solution Overview
Problem
Social networking systems face challenges in effectively determining and ranking relevant location nodes for users based on their social graph connections, leading to suboptimal search results and user engagement.
Innovation Solution
The social networking system assesses location nodes by analyzing edges and attributes within the social graph, assigning values based on relevance, engagement levels, advertising sponsorship, and operational factors to rank location nodes for users, thereby enhancing search results and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system ranks location nodes based on basic search criteria, then search coverage is maintained, but search result relevance and user engagement are suboptimal
Solution Approach 1:
The patent segments the ranking process into multiple independent scoring components: social graph analysis (separating direct connections from friend-of-friend connections), engagement metrics (likes, check-ins, reviews), and operational factors (hours, capacity). Each component is calculated separately and then aggregated to produce the final ranking, allowing the system to maintain high relevance without overwhelming complexity.
Solution Approach 2:
The ranking system is designed to be dynamic rather than static. It continuously updates scores based on real-time user actions (new check-ins, likes, reviews) and adjusts rankings accordingly. The system adapts to changing user preferences and social graph structures, ensuring search results remain relevant as the social network evolves.
2Measurement precision
If the system analyzes comprehensive social graph data to improve ranking accuracy, then search result quality improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of social graph connections and engagement metrics in advance, maintaining pre-computed scores for each location node. When a user performs a search, the system retrieves and aggregates these pre-computed scores rather than analyzing the entire social graph from scratch, dramatically reducing processing time while maintaining ranking accuracy.
Solution Approach 2:
The patent applies different levels of analysis depth to different aspects of the ranking. Direct social connections (first-degree friends) receive higher weight and more detailed analysis compared to distant connections. The system focuses computational resources on the most influential factors (direct connections, recent engagement) rather than uniformly analyzing all social graph data.
3Productivity
If the system prioritizes location nodes with high social engagement, then user engagement increases, but locations with lower engagement may be unfairly suppressed
Solution Approach 1:
The system applies engagement-based weighting partially rather than exclusively. While high-engagement locations receive boosted scores, the system ensures minimum visibility thresholds are met for all locations. Low-engagement locations still appear in results if they meet basic relevance criteria, preventing complete suppression while still rewarding popular venues.
Solution Approach 2:
The final ranking score is a composite of multiple factors: social graph proximity, engagement metrics, operational quality, and user preferences. No single factor dominates completely. This composite approach allows high-engagement locations to rise in ranking while maintaining fair representation for other locations that excel in different areas (e.g., operational quality or user proximity).
Data Source
AI summary
In one embodiment, a computing device of a network environment may receive a search query comprising location parameters. The computing device may identify locations matching the search query. The computing device may access a particular record corresponding to the location, wherein the record indicates actions by users performed on the network environment with respect to the location. The computing device may determine, for each identified location, one or more counts of one or more types of actions. The computing device may generate a search-results page comprising references corresponding to the identified locations. Each reference may display the determined counts for the respective identified location. The references may be listed in ranked order based at least in part on their respective counts. The computing device may send to a client device of a user instructions for presenting the search-results page to the user.