Location Determination Using Social Graph Confidence Scores
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Solution Overview
Problem
Social networking systems face challenges in accurately determining a user's location and ranking or suppressing entities in search results, particularly in complex social graphs with numerous nodes and edges, where existing methods lack precision and relevance.
Innovation Solution
The system determines a user's likely location using geographic information and social networking data, identifies candidate place-entities with confidence scores, ranks them based on user interactions, and suppresses duplicates or less relevant entities by analyzing duplication values and social graph information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses geographic-location information and social-networking information to determine user location, then location determination accuracy is improved, but the complexity of data processing increases
Solution Approach 1:
The system segments the complex determination process into distinct modules: receiving geographic-location information, receiving social-networking information, identifying candidate place-entities, and determining confidence scores. This modular approach manages complexity by breaking down the overall task into manageable, independent processing stages.
Solution Approach 2:
The system introduces candidate place-entities as intermediary objects between raw location data and final location determination. These candidates serve as intermediate results that bridge the gap between multiple data sources and the final confident location identification, simplifying the overall decision process.
2Measurement precision
If the system identifies multiple candidate place-entities and determines confidence scores, then location determination accuracy is improved, but the time required for processing increases
Solution Approach 1:
The system performs partial action by identifying multiple candidate place-entities rather than seeking a single definitive match immediately. By generating a ranked list of candidates with confidence scores, the system achieves sufficient accuracy without exhaustively processing all possible locations, balancing precision with efficiency.
Solution Approach 2:
The system performs preliminary action by pre-identifying candidate place-entities and their associated confidence scores before final location determination. This preliminary ranking allows the system to quickly select the most likely location without re-evaluating all candidates, reducing overall processing time.
3Reliability
If the system ranks place-entities based on user interactions and suppresses duplicates, then search result relevance is improved, but the complexity of analysis increases
Solution Approach 1:
The system uses feedback from user interactions (such as check-ins, likes, and shares) to rank place-entities and suppress duplicates. This feedback mechanism continuously improves search result relevance by learning from user behavior patterns, while the automated nature of the feedback processing manages analytical complexity.
Solution Approach 2:
The system changes parameters by using duplication-values and interaction frequencies as ranking criteria. By transforming raw interaction data into standardized parameters for comparison and ranking, the system improves result relevance while managing analysis complexity through parameter standardization.
Data Source
AI summary
In one embodiment, a method includes sending information associated with multiple candidate place-entities to a user, receiving a selection of one candidate place-entity by the user, and updating the rating of the selected candidate place-entity on an online social network. In particular, the sending is based on calculated probabilities the candidate place-entities are associated with a particular geographic location.