Proximity Coefficient Search for Social Network Entities
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Solution Overview
Problem
Social networking systems face challenges in accurately suggesting users or locations that a user has met or visited, especially when the user has a common name, due to limited information or lack of interaction history.
Innovation Solution
The system generates search results by calculating a proximity coefficient based on geographic proximity, using location data such as GPS, BLUETOOTH beacons, and socially relevant activities, and records this in a proximity graph to suggest entities that have been near the querying user, even with limited information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search methods are used based on user profiles and interaction history, then search results can be generated for users with sufficient information, but search accuracy deteriorates for users with common names or limited information
Solution Approach 1:
The patent introduces a new dimension for search - geographic proximity - beyond traditional profile-based dimensions. By calculating proximity coefficients based on location data and creating proximity graphs that represent spatial relationships, the system enables accurate search results for users with common names or limited information through a completely different attribute space.
2Measurement precision
If location data is collected and processed to calculate proximity coefficients, then search accuracy for new users improves, but system complexity increases
Solution Approach 1:
The system segments the complex search problem into distinct components: location data collection, proximity coefficient calculation, proximity graph construction, and search result generation. Each component is handled by separate modules (location-services system, proximity-coefficient system, social-networking system), making the overall complex system manageable and maintainable.
Solution Approach 2:
The proximity graph serves as an intermediary data structure that mediates between raw location data and final search results. Instead of directly processing location data during search, the system pre-processes location information into proximity graphs with calculated proximity coefficients, simplifying the actual search operation.
3Adaptability or versatility
If proximity-based search is implemented using location data, then the ability to find recently met entities improves, but data privacy concerns increase
Solution Approach 1:
The system applies different quality levels of location data processing to different users and contexts. Location data is collected and processed only for users who have opted in, and the proximity graphs are built with selective inclusion of entities based on threshold criteria, creating localized privacy protection rather than a one-size-fits-all approach.
Data Source
AI summary
In one embodiment, an online social network may receive, from a client system of a first user of the online social network, a search query inputted by the first user; and identify one or more of the entities matching the search query. At least a portion of the entities being identified based at least in part on a recorded geographic proximity between the first user and each identified entity. The online social network may access one or more proximity coefficients for the identified entities from a proximity-coefficient system of the online social network; calculate a score for each identified entity based at least in part on the value of the accessed proximity coefficients for the identified entity and social-graph information associated with the identified entity; and send, to the client system of the first user in response to the received search query, search results for display to the first user.