Social Graph Search Ranking with Segmented Re-Ranking
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Solution Overview
Problem
Existing search engines fail to effectively rank search results based on social context and user interaction, leading to irrelevant or unpersonalized search outcomes in social networking systems.
Innovation Solution
A computer-implemented search tool that utilizes a social graph to rank search results by considering user interactions, edge attributes, and node values within a social networking system, incorporating factors like 'likes,' 'check-ins,' and engagement levels to provide personalized and contextually relevant search outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search results are ranked using traditional search algorithms only, then search speed and simplicity are maintained, but search result relevance and personalization deteriorate due to lack of social context consideration
Solution Approach 1:
The patent segments the ranking process into two distinct phases: (1) generating an initial set of candidate results using traditional search algorithms, and (2) re-ranking these candidates using social context factors. This segmentation allows the system to incorporate complex social graph analysis only when needed for final ranking, rather than using it for all search operations, thus balancing relevance improvement with system complexity management.
Solution Approach 2:
The system applies social context-based re-ranking only to the top candidate results (e.g., top 100 results) rather than all search results. This partial action approach focuses computational resources on the most relevant results where social context has the greatest impact, improving personalization while avoiding excessive processing of less relevant results.
2Measurement precision
If social graph data is integrated into search ranking, then personalized and contextual search results are improved, but computational resources and processing time increase
Solution Approach 1:
The system pre-computes and stores social context metrics (such as user interaction frequencies, relationship strengths, and engagement patterns) in the social graph database before search queries are executed. This preliminary action allows the search process to quickly retrieve preprocessed social context data rather than computing it in real-time, significantly reducing computational resource consumption during actual search operations.
Solution Approach 2:
The system selectively applies social context factors only to specific search results that are most likely to benefit from personalization (e.g., results related to users' connections, interests, or recent interactions). Rather than uniformly processing all results with full social graph analysis, the system applies enhanced processing locally to high-priority candidates, reducing overall computational overhead.
3Reliability
If comprehensive social context factors are considered in ranking, then search result quality and user satisfaction are improved, but system complexity and data processing requirements worsen
Solution Approach 1:
The system extracts and isolates specific social context factors (such as user relationships, interaction history, and engagement metrics) from the comprehensive social graph data, selecting only the most relevant factors for each search query. This extraction process filters out unnecessary social context information, reducing data processing complexity while maintaining search result quality through focused use of key social signals.
Data Source
AI summary
In one embodiment, a method including maintaining an index of a plurality of nodes of a social graph, each node being associated with an assigned value, wherein the value for each node is calculated based at least in part on one or more factors. The method further includes receiving, from a client device of a first user, a query from the first user, searching the index to identify a top N nodes having the highest assigned values that match the query, ranking the identified nodes based at least in part on the query, and sending, to the client device of the first user for display, a search-results interface responsive to the received query, the search-results interface comprising M search results corresponding to the top M ranked nodes, respectively.